Adaptation and Validation of School Burnout Inventory-Bangla and Its Predictive Factors Among Adolescents

Abstract

School burnout (SB) among adolescents is a growing concern, yet there has been limited research in Bangladesh. This study aims to address this gap by validating the School Burnout Inventory (SBI) in Bangla and investigating the predictive factors of SB among high-school-going adolescents in Bangladesh. The study employed a cross-sectional design, recruiting participants from various high schools across urban and rural areas. Data were collected through self-reported questionnaires, which included information related to demographics, COVID-19, school burnout, daytime sleepiness, insomnia, and depression. The SBI-Bangla demonstrated high reliability and validity. The mean score of SB was 20.26 (±7.84). Significant differences in SB levels were observed across different age groups, grades, locations, mother’s education levels, smoking status, and self-reported COVID-19 infection status. Multiple linear regression identified age (B = 0.647, p = .010), location (B = −1.043, p = .034), depression (B = 0.270, p < .001), daytime sleepiness (B = 0.208, p < .001), and insomnia (B = 0.662, p < .001) as significant predictors of SB. The final model explained 12.3% of the variance in SB scores. Enhancing sleep hygiene and addressing psychological issues may help reduce academic burnout. This study provides valuable baseline data that can inform future research and policy formulation aimed at reducing adolescent SB in Bangladesh.

Keywords

Adolescents burnout emotional exhaustion depression sleep insomnia

Introduction

School burnout (SB) represents a critical psychological syndrome that emerges from chronic academic stress and is characterized by a triad of symptoms: emotional exhaustion, cynicism, and reduced efficacy in academic tasks (Salmela-Aro, Kiuru, et al., 2009; Schaufeli, Leiter, et al., 2002). Emotional exhaustion pertains to feelings of being overwhelmed and drained by the demands of academic work. Cynicism involves a growing sense of detachment from school activities, which can manifest as negative attitudes towards schoolwork and a reduced sense of academic competence. Reduced efficacy refers to a diminished sense of personal achievement and capability in academic contexts (Salmela-Aro, Kiuru, et al., 2009; Schaufeli, Martinez, et al., 2002). The emotional exhaustion component typically results from excessive academic pressures and a high workload, while cynicism and inefficacy stem from feelings of inadequacy and a lack of engagement with school-related tasks.

Academic stress is a significant contributor to school burnout among adolescents (Gao, 2023). Common stressors include high expectations from parents and teachers, an overwhelming academic workload, pressure to excel in national examinations, and constant peer competition. These stressors, combined with factors such as school activities, feelings of inadequacy, lack of interest, and family concerns, can induce SB (Lacombe et al., 2023; Walburg, 2014). The repercussions of burnout extend beyond immediate academic performance, significantly impacting students’ overall well-being and mental health. High levels of SB are associated with substantial psychological issues, including depression and anxiety, which can further impair academic outcomes and personal life (Fiorilli et al., 2017; Salmela-Aro, Savolainen, & Holopainen, 2009). Moreover, burnout can compromise attentional capacities and problem-solving abilities, and in extreme cases, lead to suicidal ideation (Madigan & Curran, 2021; Wang et al., 2020). This highlights the urgent need to understand and address SB among adolescents to promote a healthier and more supportive educational environment.

Theoretical Framework and Literature Review

The Job Demands-Resources model provides a valuable framework for understanding burnout. According to this model, burnout arises from an imbalance between job demands and job resources. In the context of school burnout, academic demands (e.g., heavy workload, high expectations) can exceed available resources (e.g., support from teachers and family), leading to burnout (Demerouti et al., 2001). This model helps explain how excessive academic demands and inadequate support contribute to burnout among students. Besides, The Conservation of Resources theory posits that burnout occurs when individuals perceive a threat of resource loss or experience actual resource loss without adequate resource gain (Hobfoll, 1989). This theory is applicable to school burnout as students may feel threatened by the loss of personal resources, such as time and energy, due to overwhelming academic pressures. The lack of sufficient resources to manage these demands can exacerbate feelings of burnout.

Burnout has been extensively studied across various populations, including high school students (Walburg, 2014), secondary school teachers (García-Carmona et al., 2019), medical students (Ishak et al., 2013), university students (Özhan, 2021), physicians (Rotenstein et al., 2018) and so forth. For instance, among healthcare professionals, such as physicians, burnout rates are alarmingly high. For example, a meta-analysis reported a 67% prevalence rate of burnout among physicians, with specific rates of 72% for emotional exhaustion, 68.1% for depersonalization, and 63.2% for reduced personal accomplishment (Rotenstein et al., 2018). Mental health professionals also exhibit high burnout levels, with a meta-analysis indicating a 40% prevalence of emotional exhaustion, 22% for depersonalization, and 19% for low personal accomplishment (O’Connor et al., 2018).

In the context of university students from low and middle-income countries, burnout prevalence varies. A meta-analysis by Kaggwa et al. (2021) reported a pooled burnout prevalence of 12.1%, with significant rates of emotional exhaustion, cynicism, and reduced professional efficacy. Specifically, 27.8% endured emotional exhaustion, 32.6% showed cynicism, and 29.9% had reduced professional efficacy. In France, a study of 493 adolescents revealed that burnout was most prevalent among 10th graders (84.21%), followed by 12th graders (73.17%), 11th graders (55.26%), ninth graders (49.53%), seventh graders (40.82%), eighth graders (34.07%), and sixth graders (12.64%) (Simoës-Perlant et al., 2023). In Sri Lanka, 28.8% of college students experienced burnout, influenced by satisfaction with the school environment, curriculum, favorite subjects, parental and teacher support, and parental expectations (Wickramasinghe et al., 2018). Among adolescents, burnout rates ranged from 10.9% to 14% in various countries (Tuominen-Soini & Salmela-Aro, 2014; Virtanen et al., 2014; Zhang et al., 2013), with girls reporting higher academic burnout than boys (Vinter et al., 2021). These studies collectively highlight the extensive research on burnout across different groups. Based on the discussion, we hypothesize the following:

• Hypothesis 1: There will be significant differences in SB levels based on demographic factors such as age, gender, and academic grade among adolescents.

The relationship between burnout and various psychological and physiological factors has been well-documented. There is a robust association between burnout and depression, with burnout often serving as a predictor of depressive symptoms and vice versa (Salmela-Aro, Savolainen, & Holopainen, 2009; Schonfeld & Bianchi, 2016). Meta-analytic studies have demonstrated a moderate to strong correlation between burnout and depression, highlighting the bidirectional nature of this relationship (Koutsimani et al., 2019). Sleep disturbances also play a significant role in the development of burnout. Disrupted sleep patterns, such as daytime sleepiness and insomnia, are known to exacerbate burnout symptoms (Gomes et al., 2011; Vandekerckhove & Cluydts, 2010). Research has shown that sleep disturbances can impact burnout dimensions, such as cynicism and reduced academic efficacy, thereby influencing overall academic performance (Lehto et al., 2019; Pagnin et al., 2014). Individuals with obstructive sleep apnea, for instance, are at a higher risk of experiencing burnout (Guglielmi et al., 2014). Moreover, the relationship between burnout and sleep disturbances may be bidirectional, with burnout potentially leading to poor sleep and subsequently worsening burnout symptoms (Höglund et al., 2023; Salmela-Aro, Savolainen, & Holopainen, 2009). Based on the discussion, we propose the following hypotheses:

• Hypothesis 2: Depression will be a significant predictor of SB among adolescents, with higher levels of depression associating with greater SB.

• Hypothesis 3: Sleep-related issues, including daytime sleepiness and insomnia, will significantly predict SB, with poorer sleep quality associated with higher levels of burnout.

Validation of the School Burnout Inventory

The validation of burnout scales is essential for ensuring that measurement tools accurately capture the burnout phenomenon within specific cultural and contextual settings. While internationally recognized scales such as the Maslach Burnout Inventory (MBI) and the School Burnout Inventory (SBI) have been widely used, their applicability can vary across different cultural contexts. Cultural differences in attitudes toward education, familial expectations, and stress management may influence how burnout symptoms are expressed and perceived. For instance, while the MBI has been shown to be effective across different professional sectors (Schaufeli, Leiter, et al., 2002), adaptations are necessary for specific populations. The SBI, developed and validated by Salmela-Aro, Savolainen, and Holopainen (2009), has proven reliable in European contexts but necessitates modifications for other regions. Translation and validation studies are crucial for these adaptations. For example, Secer et al. (2013) validated the SBI among Turkish primary and secondary school students, demonstrating its reliability in the Turkish context. Similarly, Boada-Grau et al. (2015) confirmed the effectiveness of the SBI in Spanish-speaking university students, and Caterina et al. (2014) validated it among high school students in Italian-speaking settings.

In the context of Bangladesh, where educational pressures and cultural norms differ from Western or European settings, adapting the SBI is particularly important. This involves translating the scale into Bengali, modifying it to reflect local stressors, and conducting rigorous psychometric evaluations. The meta-analysis by Kaggwa et al. (2021) emphasizes the necessity of such cultural adaptations to ensure accurate burnout measurement and the development of effective interventions. Proper validation within the Bangladeshi context will enhance the scale’s relevance and utility, leading to more precise assessments of school burnout among adolescents and the creation of targeted support strategies. Thus, we propose:

• Hypothesis 4: The cultural adaptation of the School Burnout Inventory will demonstrated high reliability and validity in the Bangladeshi context.

Research gap and Rationale of the Study

The current state of research on burnout in Bangladesh highlights a noticeable gap in studies, especially on SB. Despite investigations into burnout among frontline doctors during the COVID-19 pandemic (Rashid et al., 2022) and among university entrance examinees pre-pandemic (Mamun et al., 2021), the area remains underexplored. Another study noted how organizational support and professional politics played a role in the burnout of public-sector doctors during the pandemic, while social support affected private-sector doctors’ burnout (Roy et al., 2017). However, burnout in high-school adolescents in Bangladesh has not been studied yet. This study aims to fill this gap by examining the prevalence and predictive factors of school burnout in this population. Besides, the study includes a cultural validation of the burnout scale, addressing the absence of validated tools for assessing burnout in Bangladesh. By investigating these aspects, this research seeks to provide valuable insights into the magnitude of school burnout among adolescents and to inform the development of effective interventions and support systems in educational settings.

Methods

Study Design, Participants, and Procedure

The study was a part of the MeLiSA study, conducted as a cross-sectional investigation among adolescents residing in the Shahzadpur sub-district of Sirajganj. Data collection occurred in November 2022 utilizing a two-stage stratified cluster sampling technique. In the initial stage, seven schools were randomly selected, comprising three from urban areas and four from rural areas, stratified based on geographical location. In the second stage, students from the seventh, eighth, and ninth grades at each selected school were randomly invited to participate. In Bangladesh, the high school includes grades 6 through 10. Approval for data collection was obtained from school authorities, and a written informed consent form was provided to students for their parents or guardians to review and approve. During data collection, both the research team and class teachers were present in each classroom to guide students and address any questions about the questionnaire.

The inclusion criteria for the study were: (i) being present in the classroom during the survey period, and (ii) being enrolled in the seventh, eighth, or ninth grade. Students who did not provide consent, had physical disabilities or were absent were excluded from the study. Additionally, participants with incomplete responses to the outcome variable were intended to be excluded from the analysis to ensure data quality and reliability, but all the participants responded the required questions and none were excluded.

Sample Size Calculation

The sample size for the present study was determined based on several factors. Using an estimated prevalence rate of 50% (for unknown previous prevalence rate as the reference), a margin of error of 5%, and a confidence interval of 95%, the initial calculation resulted in a required sample size of 854 participants. A design effect of 2, accounting for the clustering effect of the sampling technique, was also considered. Additionally, a non-response rate of 10% was taken into account to compensate for potential participant withdrawals or incomplete responses.

Measures

Sociodemographic factors

The study collected various socio-demographic information from the participants. This included age, gender, grade, location, birth order, family type (nuclear or joint), monthly family income in Bangladeshi Taka (BDT), and parental education. Furthermore, the study also collected information on the participants’ ever-smoking status. These socio-demographic variables are essential for understanding the characteristics and background of the participants, which may play a role in their experiences of burnout.

COVID-19 related information

The study also included questions related to the participants’ experiences with COVID-19. Participants were asked whether they had been self-infected with COVID-19, whether their family or friends had been infected with COVID-19, and whether they had experienced the loss of family or friends due to COVID-19. These questions were designed to gather information on the participants' direct and indirect exposure to the pandemic. The responses to these questions were collected in a binary (yes/no) format, allowing for a straightforward assessment of the participant’s experiences with COVID-19.

School Burnout Inventory

To assess school burnout, an adapted version of the School Burnout Inventory was utilized in the study (Seema et al., 2022). The scale consisted of seven items, such as “My daily task should be reduced,” and employed a 7-point Likert scale ranging from 1 (completely disagree) to 7 (completely agree). The total score on the scale ranged from 7 to 49, with higher scores indicating higher levels of student burnout. The reliability of the scale was assessed in the present study using Cronbach’s alpha, which measures the internal consistency of the items. Previously, the scale had demonstrated good internal consistency with a Cronbach’s alpha coefficient of 0.87 (Seema et al., 2022). In the present study, the scale was translated and validated following a forward and backward translation procedure outlined by Beaton et al. (Beaton et al., 2000). The psychometric properties of the scale, including reliability and validity measures, are detailed in the results section of this study. The Bangla version of the School Burnout Inventory is provided in the supplemental file.

Daytime Sleepiness

Daytime sleepiness was evaluated using the Pediatric Daytime Sleepiness Scale (Drake et al., 2003). This scale consists of eight items designed to assess various aspects of daytime sleepiness experienced by individuals. Participants were asked to rate each item on a 5-point Likert scale, ranging from 0 (never) to 4 (always). The scale includes items such as “How often do you have trouble getting out of bed in the morning?” and “Are you usually alert most of the day?” It is important to note that item number 3, which asks about alertness throughout the day, is reverse-scored to ensure consistency in the scoring direction (Drake et al., 2003). Higher scores on the scale indicate a higher level of daytime sleepiness, with the total score ranging from 0 to 32 (Drake et al., 2003).

Insomnia

Insomnia was measured using the short version of the Insomnia Severity Index (Kraepelien et al., 2021). This version consists of two items that aim to capture key aspects of insomnia. Participants were asked to rate their satisfaction with their current sleep pattern and the extent to which their sleep interfered with daily functioning on a 5-point Likert scale, ranging from 0 (very satisfied) to 4 (very dissatisfied) with a possible score range between 0 to 8. To determine the presence of insomnia, a cutoff score of ≥6 was utilized. This threshold has been shown to yield a sensitivity of 84% and a specificity of 76%, indicating its effectiveness in identifying individuals with insomnia symptoms (Kraepelien et al., 2021). The scale was previously used in a Bangladeshi sample (Hasan et al., 2021).

Depression

Depression was assessed using the Bangla Patient Health Questionnaire (PHQ-9) (Kroenke et al., 2001; Rahman et al., 2022). The scale has nine items (e.g., “Little interest or pleasure in doing things?”) with a 4-point Likert scale (0 = not at all to 3 = more than half of the days). The total score ranges from 0 to 27, with a higher score on the score depicting a higher level of depression. While it was initially developed for adults, research has demonstrated its validity and reliability in adolescent populations (Burdzovic Andreas & Brunborg, 2017; Johnson et al., 2002; Richardson et al., 2010). In the present study, the scale showed good internal consistency with a Cronbach’s alpha coefficient of 0.75.

Statistical analysis

Data were first entered, cleaned, and ready for analysis using Microsoft Excel 2019. The categorical variables were presented with frequency percentages, and the continuous variables were presented as mean and standard deviation. Normal distribution was checked using the skewness and kurtosis values of the variables. To measure the mean differences between SB and study variables, t test or one-way ANOVA was conducted considering the variable categories. A post=hoc comparison was also performed using the least significant difference test when there was a significant mean difference among more than two groups. A Pearson correlation coefficient was calculated to examine the relationship between SB, depression, daytime sleepiness, and insomnia. In addition, multiple linear regression models were developed using SB as the dependent variable. Significant variables in the bivariate analysis (e.g.,t test/ANOVA, and Pearson correlation) were included in the linear regression model to find a better model fit. The assumptions for multiple regression were checked using variance inflation factor (<2), tolerance (>0.2), histogram, homoscedasticity, and normality plot. All the test results were significant at p < .05 with a 95% confidence interval. The Statistical Package for Social Science (SPSS) software version 25 was used for all the analyses.

An exploratory factor analysis (EFA) was conducted to investigate the underlying factor structure of the data. Before performing the EFA, the suitability of the data for factor analysis was assessed. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.864, exceeding the recommended threshold of 0.6, indicating that the sample was adequate for factor analysis. Bartlett’s test of sphericity was significant (p < .001), confirming that the correlation matrix was suitable for the analysis. The initial factor extraction was guided by eigenvalues greater than 1 and a visual inspection of the scree plot. The analysis revealed a single-factor solution, which accounted for 45.24% of the total variance. All factor loadings were above the conventional threshold of 0.32, suggesting that no items required deletion (Tabachnick et al., 2007). Following the EFA, a confirmatory factor analysis was conducted to examine the structural validity of the SB components using the Analysis of Moment Structure (AMOS) software, version 23. The reliability of the scale was investigated using Cronbach’s alpha coefficient. The convergent validity of the scale was determined by establishing its relationship with the other scales. The model fit was observed by the chi-square/df statistics, the Comparative Fit Index (CFI), the Goodness Fit Index (GFI), the Tucker Lewis Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). The CFI, the GFI, and the TLI >0.9, along with RMSEA and SRMR <0.08, indicate a satisfactory model fit (Bentler, 1990; Hu & Bentler, 1999).

Ethical consideration

Prior permission was sought from the school authorities and the respective class teachers, and informed written consent was obtained from the students before their inclusion in the study. In addition, the ethical permission for conducting this study was provided by the University of South Asia. Participants were assured about the confidentiality of the data.

Results

Characteristics of the Study Participants

The characteristics of the participants are presented in Table 1. As per the results, most of the respondents belonged to the age group of 12–14 years (72.09%), whereas the mean age of the participants was 13.91 ± 0.02. The majority of them were boys (53.94%), studying in eighth grade (42.61%), were the first child of their parents (52.88%), were from a nuclear family (82.43%), had a monthly family income of less than 15,000 BDT (44.21%). In addition, 41.85% of their fathers and 55.35% of mothers had a secondary level of education, 4.8% were ever smokers, 0.3% were infected with COVID-19, 8.5% of participants’ family members/friends had a COVID-19 infection, and 2.6% family members/friends died due to COVID-19 infection (Table 1).

Table 1.

Mean Differences Between School Burnout and Study Variables.

Variable	Total (n, %)	Mean ± SD	Mean difference (95% CI)		t/F (p-value)
Socio-demographic information
Age (mean ± SD = 13.91 ± 0.02)
12–14 years	1075; 72.09	19.74 ± 7.6	−1.79 (−2.70 to −0.88)		−3.870 (<0.001)
15–17 years	416; 27.90	21.54 ± 8.17	−1.79 (−2.70 to −0.88)		−3.870 (<0.001)
Gender
Boys	807; 53.94	20.43 ± 8.03	0.37 (−0.42–1.16)		0.922 (0.357)
Girls	689; 46.05	20.06 ± 7.61	0.37 (−0.42–1.16)		0.922 (0.357)
Grade
7	395; 27.14	19.44 ± 6.79	−0.30 (−1.28 to 0.68)^a		8.942 (<0.001)
8	620; 42.61	19.74 ± 7.65	−1.73 (−2.69 to −0.79)^b*
9	440; 30.24	21.48 ± 8.69	2.04 (0.98–3.10)^c*
Location
Urban	734; 49.06	21.08 ± 7.80	1.62 (0.83–2.41)		4.019 (<0.001)
Rural	762; 50.93	19.46 ± 7.80	1.62 (0.83–2.41)		4.019 (<0.001)
Birth order
1	751; 52.88	20.41 ± 7.62	-		0.697 (0.594)
2	402; 28.30	20.02 ± 7.88
3	144; 10.14	20.35 ± 8.41
4	73; 5.14	19.85 ± 8.16
>4	50; 3.52	18.70 ± 7.41
Family type
Nuclear	1192; 82.43	20.24 ± 7.82	−0.33 (−1.40 to 0.73)		−0.613 (0.540)
Joint	254; 17.56	20.57 ± 8.16	−0.33 (−1.40 to 0.73)		−0.613 (0.540)

Family monthly income (BDT)
Less than 15000	218; 44.21	20.70 ± 8.30	-		0.923 (0.398)
15,000-30,000	182; 36.91	21.75 ± 7.55
More than 30000	93; 18.86	21.51 ±7.82
Religion
Muslim	1425; 95.3	20.30 ± 7.87	0.83 (−1.04–2.70)		0.871 (0.385)
Others	71; 4.7	19.46 ± 7.20	0.83 (−1.04–2.70)		0.871 (0.385)
Father’s education
No education	159; 12.27	19.91 ± 7.49	-		1.428 (0.233)
Primary	293; 22.62	20.83 ± 8.49
Secondary	542; 41.85	19.98 ± 7.78
Tertiary	301; 23.24	20.91 ± 7.57
Mother’s education
No education	153; 11.61	18.97 ± 6.89	−2.33 (−3.88 to −0.79)^a,g	−1.17 (−2.54 to 0.18)^c	4.226 (0.006)
Primary	275; 20.88	21.30 ± 8.63	1.15 (0.07–2.24)^b,g	−2.52 (−4.25 to −0.79)^e,g
Secondary	729; 55.35	20.15 ± 7.69	−1.34 (−2.68 to 0)^d,g
Tertiary	160; 12.14	21.49 ± 7.68	0.18 (−1.34–1.71)^f
Ever smoking
No	1380; 92.2	20.05 ± 7.63	−2.25 (−4.45 to −0.04)		−2.035 (0.045)
Yes	72; 4.8	22.31 ± 9.22	−2.25 (−4.45 to −0.04)		−2.035 (0.045)
COVID-19 related information

Personal infection
Yes	4; 0.3	27.0 ± 2.16	6.77 (3.41–10.14)		6.166 (0.007)
No	1467; 99.7	20.22 ± 7.85	6.77 (3.41–10.14)		6.166 (0.007)
Family/friends infected
Yes	126; 8.5	21.24 ± 8.14	1.06 (−0.36–2.49)		1.461 (0.144)
No	1348; 90.5	20.17 ±7.79	1.06 (−0.36–2.49)		1.461 (0.144)
Family/friends died
Yes	39; 2.6	21.82 ± 7.90	1.57 (−0.91–4.07)		1.239 (0.216)
No	1436; 97.4	20.24 ± 7.84	1.57 (−0.91–4.07)		1.239 (0.216)
Psychological symptoms
Depression
Yes	256; 17.1	24.24 ± 7.27	4.80 (3.78–5.83)		9.173 (<0.001)
No	1240; 82.9	19.43 ± 7.70	4.80 (3.78–5.83)		9.173 (<0.001)
Insomnia
Yes	36; 2.4	25.42 ± 7.46	5.28 (2.70–7.87)		4.015 (<0.001)
No	1460; 97.6	20.13 ± 7.81	5.28 (2.70–7.87)		4.015 (<0.001)

Post-hoc comparisons.

^aDifference between first two groups.

^bDifference between second and third group.

^cDifference between first and third group.

^dDifference between third and fourth group.

^eDifference between first and fourth group.

^fDifference between the second and fourth group.

^gThe mean difference is significant at the 0.05 level.

Internal Reliability Analysis of School Burnout Inventory Bangla

Table 2 presents the item-level analysis of the school burnout items. All the items had a skewness and kurtosis value ± 2.0 range suggesting they were normally distributed. Confirmatory Factor loadings for each item are presented in Figure 1. All of the factor loadings were greater than 0.32, indicating the factor loadings were acceptable (Tabachnick et al., 2007). Cronbach’s alpha was used to investigate internal consistency. The corrected item-total correlations were above 0.40, which means all items correlated with the total. In addition, Cronbach’s alpha and if an item was deleted, was in the acceptable range (Field & Miles, 2009). In the present study, Cronbach’s alpha scale was 0.79 for the SB scale (Table 2).

Table 2.

Internal Reliability Analysis of School Burnout Inventory Bangla.

SB item	Mean	SD	Skewness	Kurtosis	Corrected item-total correlation	Cronbach’s alpha if an item deleted
Item 1	2.92	1.53	0.79	−0.11	.516	.771
Item 2	2.66	1.55	1.11	0.41	.498	.774
Item 3	3.13	1.84	0.70	−0.83	.493	.776
Item 4	3.15	1.80	0.64	−0.81	.567	.760
Item 5	2.85	1.65	0.93	−0.13	.474	.778
Item 6	2.60	1.50	1.20	0.81	.597	.757
Item 7	2.95	1.77	0.95	−0.25	.542	.765

Figure 1.

Confirmatory Factor analysis of School Burnout Inventory Bangla.

Structural Validity Analysis of School Burnout Inventory Bangla

Confirmatory factor analysis examined the structural validity of the SB scale (Figure 1). Results suggest that the scale was structurally valid (Model fit: chi-square/df = 6.810 (p < .001), GFI = 0.982, CFI = 0.966, TLI = 0.949, RMSEA = 0.062, and SRMR = 0.038).

Mean Differences Between Burnout and Study Variables

Table 1 reported the mean differences between burnout and study variables. The age group showed a significant mean difference with SB, with 15–17 years having a greater mean score (t = −3.870, p < .001). A significant difference was observed between students’ grades and SB (F = 8.942, p < .001). In-depth, grades seventh and ninth, and grades eighth and ninth significantly differed in terms of SB score, where ninth grade students had comparatively higher mean scores than seventh grade and eighth grade students. Post-hoc comparisons also suggest that no education and primary education groups, no education and tertiary education groups, primary and secondary education groups, and secondary and tertiary education groups had a significant mean difference in terms of mother’s education level (p < .05). Urban students had significantly higher burn-out scores than rural ones (t = 4.019, p < .001). In addition, participants mother’s education (t = 4.226, p = .006), their ever-smoking status (t = −2.035, p = .045), self-infected with COVID-19 virus (t = 6.166, p = .007), depressive symptoms (t = 9.173, p < .001), and insomnia (t = 4.015, p < .001) significantly differed in terms of mean SB scores (Table 1).

Pearson Correlation Between Burnout and Other Variables

Table 3 reports the correlation coefficient between SB and depression, daytime sleepiness, and insomnia. SB was positively correlated with depression (r = 0.275, p < .001), daytime sleepiness (r = 0.261, p < .001), and insomnia (r = 0.200, p < .001) (Table 3).

Table 3.

Pearson Correlation Between SB, Depression, Daytime Sleepiness, and Insomnia.

Variables	Mean ± SD	1	2	3	4
SB (1)	20.26 ± 7.84	1
Depression (2)	5.93 ± 4.14	0.275^**	1
Daytime sleepiness (3)	8.74 ± 4.82	0.261^**	0.515^**	1
Insomnia (4)	2.35 ± 1.54	0.200^**	0.355^**	0.368^**	1

** Correlation is significant at the 0.01 level (2-tailed).

Multiple Linear Regression of School Burnout

Table 4 reports the result of multiple linear regression. Model 1 included the significant variables in the bivariate analysis of SB, whereas Model 2 included additional variables such as depression, daytime sleepiness, and insomnia. Both models were significant (p < .001). The final model reported that age (B = .647, p = .010), location (B = −1.043, p = .034), depression (B = 0.270, p < .001), daytime sleepiness (B = 0.208, p < .001), and insomnia (B = 0.662, p < .001) significantly predicted SB among adolescents. The final model explained the 12.3% variance by considering all the independent variables (Table 4).

Table 4.

Multiple Linear Regression of School Burnout Among Adolescents.

Variables	Model 1					Model 2
	[R² = 0.034, F (6, 1227) = 7.259, Adjusted R² = 0.030, ∆R² = 0.034, p < .001]					[R² = 0.129, F (10, 1223) = 20.181, Adjusted R² = 0.123, ∆R² = 0.095, p < .001]
	B	S.E.	95% confidence interval		Test statistics	B	S.E.	95% confidence interval		Test statistics
	B	S.E.	Lower bound	Upper bound	t (p-value)	B	S.E.	Lower bound	Upper bound	t (p-value)
Constant	20.971	9.524	2.286	39.656	2.202 (0.028)	23.528	9.062	5.748	41.308	2.596 (0.010)
Age	.813	.264	.296	1.330	3.084 (0.002)	.647	.252	.153	1.140	2.572 (0.010)
Grade^a	.102	.373	−.629	.833	.275 (0.784)	−.121	.356	−.819	.578	−.339 (0.735)
Location^b	−2.018	.505	−3.008	−1.028	−3.998 (<0.001)	−1.043	.492	−2.008	−.077	−2.118 (0.034)
Mother education^c	−.143	.300	−.731	.446	−.476 (0.634)	−.226	.285	−.786	.334	−.793 (0.428)
Ever smoking^d	2.072	.969	.172	3.973	2.140 (0.033)	.772	.932	−1.056	2.600	.828 (0.408)
COVID-19 self-infection^e	−5.758	4.413	−14.415	2.899	−1.305 (0.192)	−7.458	4.203	−15.703	.787	−1.775 (0.076)
Depression						.270	.060	.153	.388	4.511 (<0.001)
Daytime sleepiness						.208	.053	.105	.311	3.965 (<0.001)
Insomnia						.662	.146	.376	.948	4.539 (<0.001)

^a1 = Grade 7, 2 = Grade 8, 3 = Grade 9.

^b1 = Urban, 2 = Rural.

^c1 = No education, 2 = Primary, 3 = Secondary, 4 = Tertiary.

^d1 = No, 2 = Yes.

^e1 = Yes, 2 = No; The coefficients were presented as B; S.E. = Standard error.

Discussion

The primary objective of the present study was to investigate school burnout (SB) and its association with sociodemographics and mental health problems including depression, daytime sleepiness, and insomnia among high-school-going adolescents in Bangladesh. Besides, the study addressed the need for culturally validated burnout assessment tools by including a cultural validation of the burnout scale for the Bangladeshi context. The analysis revealed significant differences in SB scores based on age, gender, geographical location, maternal education, history of smoking, and COVID-19 infection. Furthermore, the presence of depressive symptoms, daytime sleepiness, and insomnia significantly heightened the risk of experiencing SB. These findings highlight the multifaceted nature of burnout and emphasize the importance of addressing both individual and contextual factors, as well as mental health and sleep-related issues, in effectively preventing and managing SB.

The School Burnout Inventory was used to assess SB for the first time in the context, thus the scale was validated in the study. Results indicated that the internal consistency of the scale was 0.79, suggesting that the study’s results are reliable. The original School Burnout Inventory was translated and validated in different languages. For instance, a Turkish study among 570 primary and secondary school students found a similar internal consistency with a Cronbach’s alpha of 0.75 (Secer et al., 2013). Another study among university students also reported similar reliability for the SBI (Boada-Grau et al., 2015). The scale’s validity was measured using the Pearson correlation and confirmatory factor analysis, with results indicating a satisfactory model fit and significant correlation with other scales, supporting the validity of the scale. These findings are consistent with previous studies (Caterina et al., 2014; Secer et al., 2013). Therefore, it can be said that the scale is reliable and valid to use in the country.

Using the School Burnout Inventory – Bangla (SBI-Bangla), the present study revealed that the mean score for SB among adolescents was 20.26 (SD = 7.84) [score range 7–49]. This finding aligns with previous research conducted among Chinese nurses, which reported moderate levels of emotional exhaustion and depersonalization, as well as a high level of low personal accomplishment (Wang et al., 2015). Similar patterns were observed among frontline doctors in Bangladesh, who exhibited high levels of emotional exhaustion, depersonalization, and low personal accomplishment (Rashid et al., 2022). Notably, a study highlighted that burnout symptoms in adolescents may originate during primary education and further intensify during the transition from primary to secondary school (Parviainen et al., 2021). This indicates that the likelihood of experiencing burnout during adolescence increases with age, which is consistent with the current study’s findings (Fiorilli et al., 2017; Lee et al., 2013). Specifically, this study demonstrated that each additional year of age was associated with a 64.7% increase in burnout scores. Consequently, it is crucial to allocate greater attention and implement targeted interventions for individuals in higher age groups to effectively address and prevent burnout. These findings highlight the importance of recognizing age-related factors in the development and management of burnout among adolescents. By understanding the influence of age on burnout, appropriate strategies can be implemented to support and promote the well-being of adolescents at different stages of their educational journey.

The COVID-19 pandemic has emerged as a significant factor contributing to burnout, particularly among adolescents (Lacombe et al., 2023). The disruption caused by the pandemic, including prolonged school closures and interruptions in academic activities, has intensified psychological issues and contributed to increased burnout (Theberath et al., 2022). Adolescents who contracted COVID-19 often faced extended absences from school, leading to academic delays and heightened pressure upon their return. This backlog of missed coursework, combined with the anxiety related to the pandemic itself, has compounded feelings of SB (Simoës-Perlant et al., 2023). This association highlights the broader impact of global health crises on mental well-being and educational outcomes.

The findings of the present study are in line with previous research, highlighting a significant relationship between SB and depression (Koutsimani et al., 2019). This consistent association underscores the impact of depression on the development and exacerbation of SB symptoms. In support of this, a study conducted among university entrance test-taking students revealed that depression increased the likelihood of developing burnout by 2.50 times, whereas those depressed students who failed to get admission to a university were found to be at even higher risk of burnout compared to normal counterparts (Mamun et al., 2021). These findings are also supported by longitudinal research, which identified depression as a strong predictor of adolescent burnout (Salmela-Aro, Savolainen, & Holopainen, 2009). Moreover, a meta-analysis of 37 studies among nurses revealed a positive association between burnout and depression, further reinforcing the link between these two conditions (Chen & Meier, 2021). The relationship between depression and burnout can be attributed to various factors. For instance, depression may arise from the inability to meet school demands and the experience of stressful life events, both of which can contribute to the development of burnout symptoms. However, it is also important to acknowledge that burnout might contribute to depression, highlighting the complexity of the causality between these conditions. Therefore, while addressing and managing school demands effectively is crucial for protecting students from burnout, a comprehensive approach that considers both burnout and depression is essential. By focusing on alleviating depression symptoms and providing adequate support to students in managing school-related stress, it is possible to mitigate the risk of burnout and promote their overall well-being.

Several studies have identified sleep problems as a potential contributing factor in the development of burnout over time (Li et al., 2020; Liu et al., 2021). For instance, a study involving 127 medical students revealed that daytime sleepiness increased the risk of experiencing emotional exhaustion and cynicism by 1.21 times and 2.47 times, respectively. Besides, it was found to decrease academic efficacy by 0.86 times, indicating the detrimental impact of sleepiness on burnout symptoms (Pagnin et al., 2014). Further supporting this association, a study involving 350 adults demonstrated a positive correlation between sleep quality and burnout. The findings suggested that higher SB scores were associated with poorer sleep quality, highlighting the reciprocal relationship between these two variables (May et al., 2020). Similarly, among 555 Finnish secondary school students, daytime sleepiness, tiredness, and poor sleep quality were significantly associated with increased burnout scores (Lehto et al., 2019). One possible explanation for the observed link between sleep problems and burnout is the concern students have regarding their academic achievements. This worry can lead to increased levels of stress and anxiety, ultimately resulting in reduced sleep quality (Gomes et al., 2011). These findings underscore the importance of recognizing the role of sleep problems in the development and progression of burnout. By addressing and promoting healthy sleep habits, educational institutions and healthcare professionals can potentially mitigate the risk of burnout among students and individuals in various settings.

It is important to acknowledge the limitations of this study. Firstly, the cross-sectional design employed in this study prevents the establishment of a causal relationship between variables. To address this limitation and provide stronger evidence, future research using longitudinal designs is necessary to examine the temporal nature of the association between SB and its contributing factors. Secondly, the study did not capture information regarding specific stressful school life events, which could potentially influence SB levels. Including such information in future studies would enhance our understanding of the contextual factors contributing to SB among adolescents. Thirdly, the Daytime Sleepiness Scale was not validated in the sample, though we used a forward and backward translation procedure as suggested by Beaton et al. (Beaton et al., 2000). Despite these limitations, this study holds significant value as it is the first of its kind to investigate SB among adolescents in Bangladesh. By utilizing culturally adapted and validated assessment tools, the study ensures the appropriateness of measuring SB within the local cultural context. The findings of this study serve as a crucial foundation for future research in this area, especially in terms of validation of the scale. Policymakers, educators, and healthcare professionals can utilize these findings to inform the development of targeted interventions and support systems aimed at preventing and addressing SB among adolescents in Bangladesh. Further studies can build upon these findings, addressing the limitations identified, to provide a more comprehensive understanding of the complex factors contributing to adolescent SB in this population.

Conclusions

In conclusion, this study highlights several important findings regarding adolescent school burnout. First, the cultural validation of the scale seems reliable and valid for assessing the school burnout of Bangla-speaking adolescents. The results also indicate that higher age groups, depression, and sleep problems play significant roles in predicting school burnout among adolescents. It is important for educators, policymakers, and healthcare professionals to recognize the impact of these factors on adolescent school burnout and to implement appropriate strategies and interventions. By addressing these predictors and targeting high-risk individuals, efforts can be made to reduce the prevalence of burnout and enhance the overall mental health and academic performance of adolescents. Further research is warranted to explore additional factors contributing to school burnout and to develop comprehensive interventions that address the multifaceted nature of this syndrome. By building upon these findings, future studies can contribute to the development of evidence-based approaches to prevent and manage school burnout among adolescents.

Supplemental Material

Supplemental Material - Adaptation and Validation of School Burnout Inventory-Bangla and Its Predictive Factors Among Adolescents

Supplemental Material for Adaptation and Validation of School Burnout Inventory-Bangla and Its Predictive Factors Among Adolescents by Firoj Al-Mamun, Mohammed A. Mamun, Moneerah Mohammad ALmerab, Johurul Islam, and Mohammad Muhit in Psychological Reports

Footnotes

Acknowledgements

The author would like to thank all the study participants.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the University of South Asia, Dhaka, Bangladesh and the CHINTA Research Bangladesh, Dhaka, Bangladesh. Dr. ALmerab acknowledges the funding support currently receiving from Princess Nourah Bint Abdulrahman University Researchers Supporting Project (PNURSP2025R563), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.

Correction (February 2025):

The article has been updated with the Funding statement since its original publication. See correction notice for more details.

ORCID iD

Firoj Al-Mamun

Data Availability Statement

The research data is available from the corresponding author upon reasonable request.*

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Firoj Al Mamun is a lecturer in the Department of Public Health at the University of South Asia and is affiliated with CHINTA Research Bangladesh. He serves as the coordinator of the Dr. Muhit Centre for Advanced Research at the university. His research interests span various fields of public health and epidemiology, with publications in prestigious academic journals.

Mohammed A. Mamun is a faculty member at the Department of Public Health, University of South Asia, and the Director of CHINTA Research Bangladesh. His diverse public health research has led to extensive contributions in esteemed journals, including The Lancet, with over 10,000 citations.

Moneerah Mohammad Almerab is a psychologist and faculty member at the Department of Psychology, College of Education and Human Development, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. Her research focuses on psychiatric assessment, psychiatric epidemiology, and global mental health.

Johurul Islam has worked in public health for several years and is currently the Deputy Registrar at the University of South Asia, Dhaka, Bangladesh. He also serves as the Program Manager at CSF Global. His primary research interests include public health ophthalmology and other public health areas.

Mohammad Muhit is a medical doctor, epidemiologist, and public health specialist with a PhD from the London School of Hygiene & Tropical Medicine. He led Bangladesh’s first national childhood blindness study and the Bangladesh Childhood Cataract Campaign (2004-2009), which provided free cataract surgery to over 10,000 children. Dr. Muhit has also helped establish cerebral palsy registers in multiple low- and middle-income countries. His research focuses on public health ophthalmology, cerebral palsy, and disability in LMICs.

References

Beaton

D. E.

Bombardier

Guillemin

Ferraz

M. B.

(2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine, 25(24), 3186–3191. https://doi.org/10.1097/00007632-200012150-00014

Bentler

P. M.

(1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Boada-Grau

Merino-Tejedor

Sánchez-García

J. C.

Prizmic-Kuzmica

A. J.

Vigil-Colet

(2015). Adaptation and psychometric properties of the SBI-U scale for Academic Burnout in university students. [Adaptación y propiedades psicométricas de la escala SBI-U sobre Burnout Académico en estudiantes universitarios]. Annals of Psychology, 31(1), 290–297. https://doi.org/10.6018/analesps.31.1.168581

Burdzovic Andreas

Brunborg

G. S.

(2017). Depressive symptomatology among Norwegian adolescent boys and girls: The patient health questionnaire-9 (PHQ-9) psychometric properties and correlates. Frontiers in Psychology, 8, 887. https://doi.org/10.3389/fpsyg.2017.00887

Caterina

Virna

Simona

D. S.

Carlo

D. C.

Ottavia

(2014). School Burnout Inventory with Italian students of high school. Psicologia Clinica dello Sviluppo, 18, 403–424. https://doi.org/10.1449/78365

Chen

Meier

S. T.

(2021). Burnout and depression in nurses: A systematic review and meta-analysis. International Journal of Nursing Studies, 124(1), Article 104099. https://doi.org/10.1016/j.ijnurstu.2021.104099

Demerouti

Bakker

A. B.

Nachreiner

Schaufeli

W. B.

(2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499

Drake

Nickel

Burduvali

Roth

Jefferson

Badia

(2003). The pediatric daytime sleepiness scale (PDSS): Sleep habits and school outcomes in middle-school children. Sleep, 26(4), 455–458. https://doi.org/10.1093/sleep/26.4.455

Field

A. P.

Miles

(2009). Discovering statistics using SPSS:(and sex and drugs and rock’n’roll).

10.

Fiorilli

De Stasio

Di Chiacchio

Pepe

Salmela-Aro

(2017). School burnout, depressive symptoms and engagement: Their combined effect on student achievement. International Journal of Educational Research, 84(84), 1–12. https://doi.org/10.1016/j.ijer.2017.04.001

11.

Gao

(2023). Academic stress and academic burnout in adolescents: A moderated mediating model. Frontiers in Psychology, 14, Article 1133706. https://doi.org/10.3389/fpsyg.2023.1133706

12.

García-Carmona

Marín

M. D.

Aguayo

(2019). Burnout syndrome in secondary school teachers: A systematic review and meta-analysis. Social Psychology of Education, 22(1), 189–208. https://doi.org/10.1007/s11218-018-9471-9

13.

Gomes

A. A.

Tavares

de Azevedo

M. H. P.

(2011). Sleep and academic performance in undergraduates: A multi-measure, multi-predictor approach. Chronobiology International, 28(9), 786–801. https://doi.org/10.3109/07420528.2011.606518

14.

Guglielmi

Jurado-Gámez

Gude

Buela-Casal

(2014). Job stress, burnout, and job satisfaction in sleep apnea patients. Sleep Medicine, 15(9), 1025–1030. https://doi.org/10.1016/j.sleep.2014.05.015

15.

Hasan

Maliha

Rahman

Mamun

M. A.

(2021). Insomnia in Bangladeshi young adults during the COVID-19 pandemic: The role of behavioral factors, COVID-19 risk and fear, and mental health issues. Sleep and Vigilance, 5(2), 315–322. https://doi.org/10.1007/s41782-021-00161-5

16.

Hobfoll

S. E.

(1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513–524. https://doi.org/10.1037/0003-066X.44.3.513

17.

Höglund

Hakelind

Nordin

(2023). Risk factors for insomnia and burnout: A longitudinal population-based cohort study. Stress and Health, 39(4), 798–812. https://doi.org/10.1002/smi.3218

18.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

19.

Ishak

Nikravesh

Lederer

Perry

Ogunyemi

Bernstein

(2013). Burnout in medical students: A systematic review. The Clinical Teacher, 10(4), 242–245. https://doi.org/10.1111/tct.12014

20.

Johnson

J. G.

Harris

E. S.

Spitzer

R. L.

Williams

J. B. W.

(2002). The patient health questionnaire for adolescents: Validation of an instrument for the assessment of mental disorders among adolescent primary care patients. Journal of Adolescent Health, 30(3), 196–204. https://doi.org/10.1016/S1054-139X(01)00333-0

21.

Kaggwa

M. M.

Kajjimu

Sserunkuma

Najjuka

S. M.

Atim

L. M.

Olum

Tagg

Bongomin

(2021). Prevalence of burnout among university students in low- and middle-income countries: A systematic review and meta-analysis. PLoS One, 16(8), Article e0256402. https://doi.org/10.1371/journal.pone.0256402

22.

Koutsimani

Montgomery

Georganta

(2019). The relationship between burnout, depression, and anxiety: A systematic review and meta-analysis. Frontiers in Psychology, 10, 284. https://doi.org/10.3389/fpsyg.2019.00284

23.

Kraepelien

Blom

Forsell

Hentati Isacsson

Bjurner

Morin

C. M.

Jernelöv

Kaldo

(2021). A very brief self-report scale for measuring insomnia severity using two items from the Insomnia Severity Index - development and validation in a clinical population. Sleep Medicine, 81(2), 365–374. https://doi.org/10.1016/j.sleep.2021.03.003

24.

Kroenke

Spitzer

R. L.

Williams

J. B. W.

(2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x

25.

Lacombe

Hey

Hofmann

Pagnotta

Squillaci

(2023). School burnout after COVID-19, prevalence and role of different risk and protective factors in preteen students. Children, 10(5), 823. https://doi.org/10.3390/children10050823

26.

Lee

Puig

Lea

Lee

S. M.

(2013). Age-related differences in academic burnout of Korean adolescents. Psychology in the Schools, 50(10), 1015–1031. https://doi.org/10.1002/pits.21723

27.

Lehto

J. E.

Kortesoja

Partonen

(2019). School burnout and sleep in Finnish secondary school students. Sleep Science, 12(1), 10–14. https://doi.org/10.5935/1984-0063.20190051

28.

Zhang

Randhawa

A. K.

Madigan

D. J.

(2020). Emotional exhaustion and sleep problems in university students: Does mental toughness matter? Personality and Individual Differences, 163, Article 110046. https://doi.org/10.1016/j.paid.2020.110046

29.

Liu

Zhang

Yang

Liang

(2021). The longitudinal relationship between sleep problems and school burnout in adolescents: A cross-lagged panel analysis. Journal of Adolescence, 88(1), 14–24. https://doi.org/10.1016/j.adolescence.2021.02.001

30.

Madigan

D. J.

Curran

(2021). Does burnout affect academic achievement? A meta-analysis of over 100,000 students. Educational Psychology Review, 33(2), 387–405. https://doi.org/10.1007/s10648-020-09533-1

31.

Mamun

M. A.

Safiq

M. B.

Hosen

al Mamun

(2021). Burnout, does the university entrance test failing attribute? A Bangladeshi exploratory study. PLoS One, 16(10), Article e0258100. https://doi.org/10.1371/journal.pone.0258100

32.

May

R. W.

Bauer

K. N.

Seibert

G. S.

Jaurequi

M. E.

Fincham

F. D.

(2020). School burnout is related to sleep quality and perseverative cognition regulation at bedtime in young adults. Learning and Individual Differences, 78, Article 101821. https://doi.org/10.1016/j.lindif.2020.101821

33.

O’Connor

Muller Neff

Pitman

(2018). Burnout in mental health professionals: A systematic review and meta-analysis of prevalence and determinants. European Psychiatry, 53, 74–99. https://doi.org/10.1016/j.eurpsy.2018.06.003

34.

Özhan

M. B.

(2021). Academic self-efficacy and school burnout in university students: Assessment of the mediating role of grit. Current Psychology, 40(9), 4235–4246. https://doi.org/10.1007/s12144-021-02023-9

35.

Pagnin

de Queiroz

Carvalho

Y. T. M. S.

Dutra

A. S. S.

Amaral

M. B.

Queiroz

T. T.

(2014). The relation between burnout and sleep disorders in medical students. Academic Psychiatry, 38(4), 438–444. https://doi.org/10.1007/s40596-014-0093-z

36.

Parviainen

Aunola

Torppa

Lerkkanen

M.-K.

Poikkeus

A.-M.

Vasalampi

(2021). Early antecedents of school burnout in upper secondary education: A five-year longitudinal study. Journal of Youth and Adolescence, 50(2), 231–245. https://doi.org/10.1007/s10964-020-01331-w

37.

Rahman

M. A.

Dhira

T. A.

Sarker

A. R.

Mehareen

(2022). Validity and reliability of the Patient Health Questionnaire scale (PHQ-9) among university students of Bangladesh. PLoS One, 17(6), Article e0269634. https://doi.org/10.1371/journal.pone.0269634

38.

Rashid

Erfan Uddin

R. A.Md.

Mehedi

H. M. H.

Dhar

Bhuiyan

N. H.

Sattar

M. A.

Chowdhury

(2022). Burnout syndrome among frontline doctors of secondary and tertiary care hospitals of Bangladesh during COVID-19 pandemic. PLoS One, 17(11), Article e0277875. https://doi.org/10.1371/journal.pone.0277875

39.

Richardson

L. P.

McCauley

Grossman

D. C.

McCarty

C. A.

Richards

Russo

J. E.

Rockhill

Katon

(2010). Evaluation of the Patient Health Questionnaire-9 Item for detecting major depression among adolescents. Pediatrics, 126(6), 1117–1123. https://doi.org/10.1542/peds.2010-0852

40.

Rotenstein

L. S.

Torre

Ramos

M. A.

Rosales

R. C.

Guille

Sen

Mata

D. A.

(2018). Prevalence of burnout among physicians: A systematic review. JAMA, 320(11), 1131–1150. https://doi.org/10.1001/jama.2018.12777

41.

Roy

van der Weijden

de Vries

(2017). Relationships of work characteristics to job satisfaction, turnover intention, and burnout among doctors in the district public-private mixed health system of Bangladesh. BMC Health Services Research, 17(1), 421. https://doi.org/10.1186/s12913-017-2369-y

42.

Salmela-Aro

Kiuru

Leskinen

Nurmi

J.-E.

(2009). School burnout inventory (SBI) reliability and validity. European Journal of Psychological Assessment, 25(1), 48–57. https://doi.org/10.1027/1015-5759.25.1.48

43.

Salmela-Aro

Savolainen

Holopainen

(2009). Depressive symptoms and school burnout during adolescence: Evidence from two cross-lagged longitudinal studies. Journal of Youth and Adolescence, 38(10), 1316–1327. https://doi.org/10.1007/s10964-008-9334-3

44.

Schaufeli

W. B.

Leiter

M. P.

Maslach

Jackson

S. E.

(2002). Maslach burnout inventory. In Cooper

C. L.

Robertson

I. T.

(Eds.), International review of industrial and organizational psychology (pp. 49–75). Wiley.

45.

Schaufeli

W. B.

Martinez

I. M.

Pinto

A. M.

Salanova

Bakker

A. B.

(2002). Burnout and engagement in university students: A cross-national study. Journal of Cross-Cultural Psychology, 33(5), 464–481. https://doi.org/10.1177/0022022102033005003

46.

Schonfeld

I. S.

Bianchi

(2016). Burnout and depression: Two entities or one? Journal of Clinical Psychology, 72(1), 22–37. https://doi.org/10.1002/jclp.22229

47.

Secer

Halmatoc

Veyis

Ates

(2013). Adapting school burnout inventory to Turkish culture: Study of validity and reliability. Turkish Journal of Education, 2(2), 13–24.

48.

Seema

Heidmets

Konstabel

Varik-Maasik

(2022). Development and validation of the digital addiction scale for teenagers (DAST). Journal of Psychoeducational Assessment, 40(2), 293–304. https://doi.org/10.1177/07342829211056394

49.

Simoës-Perlant

Barreau

Vezilier

(2023). Stress, anxiety, and school burnout post COVID-19: A study of French adolescents. Mind, Brain, and Education, 17(2), 98–106. https://doi.org/10.1111/mbe.12346

50.

Tabachnick

B. G.

Fidell

L. S.

Ullman

J. B.

(2007) Using multivariate statistics (5). Pearson Boston.

51.

Theberath

Bauer

Chen

Salinas

Mohabbat

A. B.

Yang

Chon

T. Y.

Bauer

B. A.

Wahner-Roedler

D. L.

(2022). Effects of COVID-19 pandemic on mental health of children and adolescents: A systematic review of survey studies. SAGE Open Medicine, 10, Article 20503121221086712. https://doi.org/10.1177/20503121221086712

52.

Tuominen-Soini

Salmela-Aro

(2014). Schoolwork engagement and burnout among Finnish high school students and young adults: Profiles, progressions, and educational outcomes. Developmental Psychology, 50(3), 649. Epub 2013 Jul 29. https://doi.org/10.1037/a0033898

53.

Vandekerckhove

Cluydts

(2010). The emotional brain and sleep: An intimate relationship. Sleep Medicine Reviews, 14(4), 219–226. https://doi.org/10.1016/j.smrv.2010.01.002

54.

Vinter

Aus

Arro

(2021). Adolescent girls’ and boys’ academic burnout and its associations with cognitive emotion regulation strategies. Educational Psychology, 41(8), 1061–1077. https://doi.org/10.1080/01443410.2020.1855631

55.

Virtanen

Lerkkanen

M.-K.

Poikkeus

A.-M.

Kuorelahti

(2014). Student behavioral disengagement, school truancy and burnout in Finnish junior high schools: Latent profile analysis. ECER.

56.

Walburg

(2014). Burnout among high school students: A literature review. Children and Youth Services Review, 42, 28–33. https://doi.org/10.1016/j.childyouth.2014.03.020

57.

Wang

Ouyang

Chen

Jiang

(2020). The relationship between negative school gossip and suicide intention in Chinese junior high school students: The mediating effect of academic burnout and gender difference. Children and Youth Services Review, 117, Article 105272. https://doi.org/10.1016/j.childyouth.2020.105272

58.

Wang

Liu

Wang

(2015). Nurse burnout: Personal and environmental factors as predictors. International Journal of Nursing Practice, 21(1), 78–86. https://doi.org/10.1111/ijn.12216

59.

Wickramasinghe

N. D.

Dissanayake

D. S.

Abeywardena

G. S.

(2018). Prevalence and correlates of burnout among collegiate cycle students in Sri Lanka: A school-based cross-sectional study. Child and Adolescent Psychiatry and Mental Health, 12(1), 26. https://doi.org/10.1186/s13034-018-0238-z

60.

Zhang

Klassen

R. M.

Wang

(2013). Academic burnout and motivation of Chinese secondary students. International Journal of Social Science and Humanity, 3(2), 134–138. https://doi.org/10.7763/IJSSH.2013.V3.212

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.43 MB