REE 20(1) Riobamba ene. - abr. 2026
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BY NC ND
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ISSN-impreso 1390-7581
ISSN-digital 2661-6742
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
38
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
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39
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
40
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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M
OF
Age
20,23
2.37
N
%
Gender
Male
425
74.4
Female
146
25.6
Study condition
Regular
487
85.5
Irregular
84
14.7
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
41
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
42
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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positivo y bienestar psicológico. Investigación en Educación Médica [Internet]. 2020 [cited 17 May
36. Toctaguano Buri BS, Eugenio Zumbana LC. Calidad de sueño y su relación con el estrés académico
en estudiantes de bachillerato. Ciencia Latina Revista Científica Multidisciplinar [Internet]. 2023
37. Sabouripour F, Roslan S, Ghiami Z, Memon MA. Mediating role of self-efficacy in the relationship
between optimism, psychological well-being, and resilience among Iranian students. Front Psychol
psychology/articles/10.3389/fpsyg.2021.675645/full DOI: https://doi.org/10.3389/fpsyg.2021.675645.
38. Klainin Yobas P, Vongsirimas N, Ramirez DQ, Sarmiento J, Fernandez Z. Evaluating the relations
hips among stress, resilience and psychological well-being among young adults: a structural equation
https://link.springer.com/article/10.1186/s12912-021-00645-9 DOI: https://doi.org/10.1186/s12912-
021-00645-9.
39. Tacca D, Tacca A. Síndrome de burnout y resiliencia en profesores peruanos. Revista de Investigación
40. Godínez J. Mecanismos neurobiológicos de la resiliencia: navegando la adaptación emocional y
sa.cr/bibliotecas/bhp/cupula/v37n1/05.pdf.
41. Carazo Vargas V. Resiliencia y coevolución neuroambiental. Revista Educación [Internet]. 2018
44055139033.pdf DOI: https://doi.org/10.15517/revedu.v42i2.28137.
42. Qin Z, Liu C, Guo X, Meng C, Gao J, Lv J, et al. The influence of subjective well-being on mental
health in nursing students: the role of student cynicism. Psychol Health Med [Internet]. 2022 [cited
506.2021.2019807 DOI: https://doi.org/10.1080/13548506.2021.2019807.
43. López Astudillo A. Burnout universitario en el estudiante de ingeniería industrial. Encuentro Int Educ
view/2393.
44. Ríos Risquez MI, García Izquierdo M, Sabuco Tebar E de los Á, Carrillo Garcia C, Solano Ruiz C.
Connections between academic Burnout, resilience, and psychological well-being in nursing students:
on: https://pubmed.ncbi.nlm.nih.gov/29992596/.
45. Jorquera Gutiérrez R. Bienestar psicológico y variables sociodemográficas en una muestra de
Variables M SD Ace K
Kolmogorov Smirnov
Statistical
p
Academic Burnout
32,34
14.58
0.082
0.731
0.070
0.05
Resilience
27.51
6.92
0.496
0.664
0.071
0.05
Psychological Well-
being 33.62 4.78 -1,428 2,732 0.153 0.05
M = Mean, SD = Standard deviation, p = P-value
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
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43
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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42. Qin Z, Liu C, Guo X, Meng C, Gao J, Lv J, et al. The influence of subjective well-being on mental
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Psychological well-being
rho
p
Academic Burnout
0.462**
0.001
Resilience
0.423**
0.001
Model
R
R 2
adjusted R²
p
1
0.491
0.241
0.239
0.001
2
0.502
0.252
0.245
0.001
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
44
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Preacher
B
p
95 % CI
Constant
34,058
0.001
[31,953 – 36,162]
Academic Burnout
0.124
0.001
0.376
[0.150 0.097]
Resilience
0.130
0.001
0.187
[ 0.074 – 0.185 ]
Emotional ehaustion
0.045
0.161
0.070
[0.09 – 0.018]
Cynicism
0.235
0.001
0.282
[0.318 0.152]
Academic
ineffectiveness 0.105 0.001 0.145 [0.168 0.042]
Age
0.015
0.843
0.007
[0.08 – 0.08]
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
45
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
46
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
BIBLIOGRAPHIC REFERENCES
1. American Psychological Association. Higher education is struggling: Psychologists are navigating its
https://www.apa.org/monitor/2024/01/trends-higher-education-challenges.
2.
2024 [cited 7 May 2025]. Available on: https://www.dw.com/es/salud-mental-2024-cada-vez-m%
C3%A1s-personas-con-enfermedades-mentales/a-68449040.
REE 20(1) Riobamba ene. - abr. 2026
cc
BY NC ND
47
ISSN-impreso 1390-7581
ISSN-digital 2661-6742
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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positivo y bienestar psicológico. Investigación en Educación Médica [Internet]. 2020 [cited 17 May
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021-00645-9.
39. Tacca D, Tacca A. Síndrome de burnout y resiliencia en profesores peruanos. Revista de Investigación
40. Godínez J. Mecanismos neurobiológicos de la resiliencia: navegando la adaptación emocional y
sa.cr/bibliotecas/bhp/cupula/v37n1/05.pdf.
41. Carazo Vargas V. Resiliencia y coevolución neuroambiental. Revista Educación [Internet]. 2018
44055139033.pdf DOI: https://doi.org/10.15517/revedu.v42i2.28137.
42. Qin Z, Liu C, Guo X, Meng C, Gao J, Lv J, et al. The influence of subjective well-being on mental
health in nursing students: the role of student cynicism. Psychol Health Med [Internet]. 2022 [cited
506.2021.2019807 DOI: https://doi.org/10.1080/13548506.2021.2019807.
43. López Astudillo A. Burnout universitario en el estudiante de ingeniería industrial. Encuentro Int Educ
view/2393.
44. Ríos Risquez MI, García Izquierdo M, Sabuco Tebar E de los Á, Carrillo Garcia C, Solano Ruiz C.
Connections between academic Burnout, resilience, and psychological well-being in nursing students:
on: https://pubmed.ncbi.nlm.nih.gov/29992596/.
45. Jorquera Gutiérrez R. Bienestar psicológico y variables sociodemográficas en una muestra de
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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506.2021.2019807 DOI: https://doi.org/10.1080/13548506.2021.2019807.
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Connections between academic Burnout, resilience, and psychological well-being in nursing students:
on: https://pubmed.ncbi.nlm.nih.gov/29992596/.
45. Jorquera Gutiérrez R. Bienestar psicológico y variables sociodemográficas en una muestra de
Academic Burnout and Resilience as predictors of psychological well-being in university students
Burnout académico y resiliencia como predictores del bienestar psicológico en estudiantes universitarios
https://doi.org/10.37135/ee.04.25.03
Authors:
Apaza-Calla Emanuel Humberto - https://orcid.org/0009-0005-7909-5173
Aquize-Anco Eddy Wildmar - https://orcid.org/0000-0002-4820-9570
Affiliation:
Peruvian Union University, Professional School of Psychology, Juliaca, Peru
Correspondence:
Emanuel Humberto Apaza Calla, Peruvian Union University, Professional School of Psychology, Juliaca,
Peru, Email: emanuelapaza@upeu.edu.pe, Phone: +51 964452571, Address: Jr. Gonzáles Prada 674, Juliaca,
Puno, Peru, Postal Code: 02804, Peru
Recibido: May, 20 2025 Aceptado: December, 1 2025
ABSTRACT
This research aimed to determine whether academic Burnout and Resilience predict psychological well-being
among university students. The study employed a predictive cross-sectional associative strategy and included
a sample of 571 university students from Juliaca, with an average age of 20 (M = 20.23), of whom 74.4 %
were male. The Maslach Burnout Inventory Student Survey (MBI-SS), the Connor Davidson Resilience
Scale (CD-RISC 10), and the Psychological Well-being Scale (PWB) were used. The results indicate that the
23.9 % of the variance in psychological well-being. It is concluded that academic Burnout and Resilience are
significant predictors of psychological well-being in university students. Specifically, it was found that cynicism
predictive.
Keywords: Burnout, Psychological Resilience, Psychological Well-Being, Student Health.
RESUMEN
La presente investigación tuvo por objetivo determinar si el burnout académico y la resiliencia actúan como
predictores del bienestar psicológico en estudiantes universitarios. El estudio responde a una estrategia
asociativa de tipo predictivo transversal y contó con una muestra de 571 estudiantes universitarios de Juliaca,
con una edad promedio de 20 años (M = 20.23), donde el 74.4 % fueron varones. Se utilizó el inventario de
Maslach Burnout Encuesta Estudiantil (MBI-SS), la escala breve de Resiliencia Connor Davidson
(CD-RISC 10) y la escala de Bienestar Psicológico (BIEPS-A). Los resultados indican que la variable
de la varianza del bienestar psicológico. Se concluye que el burnout académico y la resiliencia son variables
predictoras significativas del bienestar psicológico en estudiantes universitarios. De manera específica, se
encontró que las dimensiones de cinismo e ineficacia constituyen factores negativos para el bienestar, mientras
que el agotamiento y la edad no resultaron ser variables predictoras
Palabras clave: burnout, resiliencia psicologíca, bienestar psicológico, salud del estudiante.
INTRODUCTION
The university environment is characterized by multiple academic, social, and individual demands that
constantly
that can overwhelm their coping skills and trigger mental health problems. In the United States, it has been
revealed that up to 14% of university students have considered suicide at some point, and more than 40 %
showed positive symptoms of depression.
(1)
In countries in Europe, Asia, and the Americas, it has been reported
that young people between the ages of 18 and 24 constitute 43 % of the population affected by mental illness,
with women registering higher rates.
(2)
In Latin America and the Caribbean, it is estimated that around 16
stress are the most prevalent.
(3)
been treated, of which 11,400 correspond to young people. In light of this, it is noted that inadequate stress
management, associated with psychosocial risk factors, can trigger mood disorders and leave this population
without coping resources.
(4)
the demands of their environment.
(5)
This syndrome represents the highest level of stress in the educational
(6)
Resilience
is conceptualized as the capacity for adaptation that a person possesses in the face of adverse situations that
(7)
It is a dynamic competence that is reinforced through the continuous interaction
between the person and their environment.
(8)
In relation to psychological well-being, it is understood as a
(9)
Burnout,
(10)
In the scientific literature, the following studies were identified as the main precedents for this study. In
power of 35 %. Furthermore, it was identified that Resilience acted as a partial mediator, as its inclusion reduced
(11)
In India, a study
well-being (R² = 0.308), with this variance being more influenced by coping skills in the face of stressful
stimuli.
(12)
In Portugal, a predictive model of psychological well-being showed greater variance based on the
of these factors on women.
(13)
predicted psychological well-being by 25.5 %, with academic efficacy acting as the main protective factor for
well-being.
(14)
In Canada, positive associations were found between psychological well-being, Resilience, and
satisfaction with autonomy, with a predictive power of 66 % (R² = 0.66).
(15)
In university students in Guatemala,
(R² = 0.26).
(16)
In the Canary Islands, 475 university students demonstrated moderate to high levels of academic
Burnout (ME = 33.49), indices that were significantly associated with the intention to drop out of their
studies (r = 0.710).
(17)
Furthermore, in Spain, predictors of subjective well-being were identified from the
perspective of positive psychology, highlighting that feelings of self-efficacy and intrinsic motivation best
(18)
consequently, students with a higher perception of stress tended to see themselves as less effective at coping
with their academic demands.
(19)
In Madre de Dios, it was found that the methodological migration process
psychological well-being of university students (r = -0.601).
(20)
In descriptive studies, it was found that
31.4 % of 341 Peruvian university students showed high levels of Burnout, with no significant differences
found in terms of gender and age.
(6)
Similarly, in Puerto Maldonado, 850 university students registered high
(21)
In contrast, the southern region of Peru provides little
(22)
Likewise, at the National University of Puno, it was found that 70 students showed high levels of Resilience
(74.29 %), indices that were directly related to academic commitment.
(23)
In Juliaca, academic Burnout was
observed to be a constant among university students, as 67.7 % reflected moderate levels.
(24)
Considering the above, it is essential to note that higher education students face a heavy curricular load and
high demands during their field placements. Therefore, stressful stimuli in their environment are a source of
risk that negatively impacts their sense of well-being.
(25)
Consequently, it is necessary to employ a predictive
at the national level, and specifically in the Southern Region, that associates these variables. From this
perspective, the objective of this research was to determine whether academic Burnout and Resilience
predict the psychological well-being of university students in Juliaca. Specifically, the study sought to assess
the predictability of psychological well-being based on a model composed of the dimensions of academic
Burnout and age.
METHOD
Type and design of research
An empirical study was developed using an associative strategy and a cross-sectional predictive design.
response variable, based on the independent variables of academic Burnout and Resilience.
(26)
Participants
The sampling method used was non-probabilistic convenience sampling.
(27)
The sample consisted of 571
university students from the Faculty of Engineering and Architecture at a university located in the city of
Juliaca. The average age of the participants was 20.23 years (SD = 2.37). Regarding gender, 425 were male,
and 146 were female. Furthermore, 487 students reported having regular enrollment status, while 84 reported
irregular enrollment status. Regarding their place of origin, 439 indicated they came from urban areas,
compared to 132 from rural areas. Finally, regarding their academic program, 280 students indicated they
were studying Civil Engineering, making it the most represented program (Table 1).
Table 1. Sociodemographic characteristics of the participants
M = Mean, SD = Standard deviation, n = frequency
Tools
To assess academic Burnout, the Maslach Burnout Inventory - Student Survey (MBI-SS) was used in its
(28)
The inventory consists of 15 items
regularly = 3
Resilience was measured using the Connor-Davidson Resilience Scale (CD-RISC-10), adapted for Peruvian
university students.
(29)
The instrument consists of 10 items that form a unidimensional structure. Scoring is
To measure psychological well-being, the Psychological Well-being Scale (PWB) was used, adapted to the
(30)
The scale consists of 13 items distributed across four factors (acceptance/control
of situations, autonomy, social bonds, and projects). It uses a Likert-type response category with three
Ethical aspects
The research was reviewed and subsequently approved by the Ethics Committee of the Faculty of Health
Sciences of the Peruvian Union University through certificate No. 2024-CE-FCS – UPeU-021-2024.
Likewise, the ethical principles of research outlined in the Declaration of Helsinki were respected, including
those of autonomy, justice, beneficence, and non-maleficence.
(31)
Data analysis
At the univariate level, frequency and percentage analyses were used for categorical variables. In contrast,
for numerical variables, measures of central tendency and dispersion were estimated from the distributions
(skewness and kurtosis). Subsequently, univariate normality was verified using the Kolmogorov-Smirnov
test. At the bivariate level, non-parametric correlation coefficients were used, considering a margin of error
were first verified and met,
(32)
and then the variables were subjected to multiple regression analysis to obtain
RESULTS
Descriptive analysis of numerical variables revealed that the burnout variable had a mean (M) of 32.34 and
indicating a slight concentration of scores above the mean and a slightly flat distribution. On the other hand,
(0.664), indicating a distribution concentrated around the mean. As for the psychological well-being variable,
a leptokurtic distribution.
(33)
Regarding normality, the Kolmogorov-Smirnov test showed significance values
chosen (Table 2).
Table 2. Descriptive analyses and normality tests for the variables of interest
In the correlation analysis, an inverse, highly significant, moderate-intensity relationship was evident
-
(Table 3).
Table 3. Correlations for the variables of interest
The linear regression analysis confirmed the predictive power of two models of psychological well-being.
The first model presented academic Burnout and Resilience as predictors of psychological well-being. In
contrast, the second model tested the predictive relationships between the dimensions of academic Burnout
model achieved an intervariate correlation coefficient (R = 0.491) and an adjusted coefficient of determination
second model achieved an intervariate correlation coefficient (R = 0.502) and an adjusted coefficient of
(Table 4).
Table 4. Prediction of the proposed models in the psychological well-being variable
ineffectiveness, Age. b. Dependent variable: Psychological well-being, R = multiple correlation coefficient,
adjusted R² = coefficient of determination squared, adjusted R² = adjusted coefficient of determination squa
red, p = P value.
noting that, of all the independent variables, Resilience is the only one that acts as a protective factor. It is
to be considered predictors of well-being (> 0.05) (Table 5).
Table 5. Multiple linear regression analysis
confidence interval
DISCUSSION
The main objective of this study was to determine whether academic Burnout and Resilience predict the
psychological well-being of university students. The results showed that psychological well-being can be
consistent with studies among university students in Korea, where academic Burnout was found to predict
(34)
In this sense, and considering
t
attributed to the adaptive process that students undergo both at the time of university entry and in the
methodological reintegration after the pandemic. These processes involve facing significant challenges that
(35)
Likewise, risk factors such as disorganized time management, unhealthy leisure activities, and irregular sleep
cognitive functioning. These conditions tend to reinforce the symptoms of academic Burnout and, in turn,
create difficulties across various spheres of their performance.
(36)
indicating that higher resilience indices correlate with improved well-being among university students. This
(37,38)
through their degree programs, they produce more effective adaptation and coping strategies, enabling them
to meet academic demands more effectively.
(39)
This process reflects constant maturational development in
problem-solving in the face of adversity.
(40)
In this sense, Resilience is understood as a dynamic process that
lives. University education plays an essential role in consolidating resilient competencies, specifically when
they will promote greater emotional balance and improved coping skills, ultimately translating into increased
psychological well-being and life satisfaction for the student.
(41)
Regarding the specific objectives, the dimensions of academic Burnout were proposed as predictors of
well-being in the study population. These findings are similar to those observed in students from China
(42)
which engineering students are subjected, given that their curriculum, field practices, and high teaching
demands generate a decrease in their ability to achieve their goals. This situation intensifies feelings of
frustration and ineffectiveness, leading some students to consider academic dropout as a coping mechanism.
(21)
response that allows them to distance themselves from their academic responsibilities, generating a momentary,
illusory sense of relief.
(43)
(44)
specific stimuli.
(21)
a significant predictor.
(16)
between 18 and 29 years of age tend to reflect similar levels of psychological well-being, characterized by
the pursuit of personal growth, the development of autonomy, and the construction of a life project. In this
sense, human beings reach specific milestones in the different stages of their development, so well-being
behaves similarly in different populations depending on the life stage they are in.
(45)
since it is not probabilistic. This resulted in a lack of randomness in the sample. Furthermore, because it was
a cross-sectional study, the association between variables was analyzed only at a single point in time, potentially
biasing the results. Despite these considerations, we cannot dismiss the fact that this research provides relevant
information on the interaction of these variables, as these results can, methodologically, be replicated in other
CONCLUSIONS
Regarding the general objective, Model 1, composed of academic Burnout and Resilience as independent
variables, showed that both variables predict psychological well-being, accounting for 23.9 % of the variance
Funding: Self-funded study
Conflict of interest: The authors declare that they have no conflicts of interest related to this work.
Contribution statement: All authors have participated substantially in the conception, design, writing, and
critical review of the article. Furthermore, each author has approved the final version of the manuscript and
assumes responsibility for the content presented.
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