TYPE Brief Research Report

PUBLISHED 18 December 2024

DOI 10.3389/fpsyg.2024.1434412

Burnout Assessment Tool for

OPEN ACCESS Students (BAT-S): evidence of
validity in a Chilean sample of
EDITED BY
Begoña Espejo,
University of Valencia, Spain

REVIEWED BY
Cesar Merino-Soto,
undergraduate university students
Monterrey Institute of Technology and Higher
Education (ITESM), Mexico
Irene Checa,
Marcos Carmona-Halty 1*, Karina Alarcón-Castillo 1,
University of Valencia, Spain Carla Semir-González 1, Geraldy Sepúlveda-Páez 1 and
*CORRESPONDENCE Wilmar B. Schaufeli 2,3
Marcos Carmona-Halty
mhalty@academicos.uta.cl 1
Escuela de Psicología y Filosofía, Universidad de Tarapacá, Arica, Chile, 2 Department of Psychology,
Utrecht University, Utrecht, Netherlands, 3 Research Unit Occupational & Organizational Psychology
RECEIVED 23 September 2024
and Professional Learning, KU Leuven, Leuven, Belgium
ACCEPTED 21 November 2024
PUBLISHED 18 December 2024

CITATION This brief report examines both within-network and between-network construct
​ armona-Halty M, Alarcón-Castillo K, Semir
C
Gonzalez C, Sepúlveda-Páez G and
validity of the Burnout Assessment Tool for Students (BAT-S) in a sample of 461
Schaufeli WB (2024) Burnout Assessment Tool Chilean undergraduate university students (70.9% female) ranging between 18 and
for Students (BAT-S): evidence of validity in a 58 years old (M = 21.6, SD = 4.34). The reliability analysis results showed adequate
Chilean sample of undergraduate university
students.
internal consistency for the overall burnout score and for each dimension. In
Front. Psychol. 15:1434412. addition, confirmatory factor analysis (CFA) supported a second-order factor
doi: 10.3389/fpsyg.2024.1434412 (academic burnout) and four first-order factors (exhaustion, mental distance,
COPYRIGHT cognitive impairment, and emotional impairment) solution. Moreover, the results
© 2024 Carmona-Halty, Alarcón-Castillo, of multiple-group CFA supported gender invariance. Finally, structural equation
Semir-González, Sepúlveda-Páez and
Schaufeli. This is an open-access article model (SEM) analysis showed that academic resources and academic demands are
distributed under the terms of the Creative associated with academic burnout. Overall, the BAT-S was found to be a reliable
Commons Attribution License (CC BY). The and valid tool to assess academic burnout in chilean sample of undergraduate
use, distribution or reproduction in other
forums is permitted, provided the original university students.
author(s) and the copyright owner(s) are
credited and that the original publication in
KEYWORDS
this journal is cited, in accordance with
accepted academic practice. No use, burnout assessment tool, psychometric analysis, undergraduate students, burnout,
distribution or reproduction is permitted academic burnout
which does not comply with these terms.

Introduction
Burnout is a metaphor that refers to a state of work-related mental exhaustion (Maslach
and Jackson, 1981; Maslach et al., 2001; Schaufeli et al., 2020). However, it can also be used in
relation to all activities that are structured, coercive in nature and are oriented toward
achieving specific goals, such as those performed by students (Schaufeli and Taris, 2005).
Following this line of reasoning, academic burnout traditionally describes those students who
are mentally exhausted, have a cynical and detached attitude toward their studies, and feel
incompetent as students (Schaufeli et al., 2002).
The current literature shows that academic burnout is directly related to study-related
negative emotions (Carmona-Halty et al., 2022), study holism (Sanseverino et al., 2023),
intention to drop out of school (Marôco et al., 2020), and anxiety (Popescu et al., 2023).
Conversely, it is inversely related to engagement (Wang et al., 2021), self-efficacy (Kong et al.,
2021), well-being (Yu and Chae, 2020), and achievement (Madigan and Curran, 2021).
Furthermore, based on the application of the Job Demands Resources (JD-R) model (Bakker
and Demerouti, 2017) in the academic context, academic demands (e.g., study overload) and
academic resources (e.g., teacher support), promote and prevent its occurrence, respectively

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(Lesener et al., 2020; Salmela-Aro et al., 2022; Salmela-Aro and Hadžibajramović et al., 2022, 2024). In addition, different language
Upadyaya, 2014; Zeijen et al., 2024). versions (e.g., Italian, Japanese, French, and Spanish) and a student
Research on academic burnout has mostly been conducted using version (BAT-S) are currently available.
the Maslach Burnout Inventory-Student Survey (MBI-SS) developed Despite the increasingly robust body of research generated
by Schaufeli et al. (2002). The MBI-SS is an adaptation of the Maslach around the validity of the BAT, psychometric analysis of this tool
Burnout Inventory General Survey (MBI-GS; Maslach et al., 1997) in an academic context is still scarce (for a review, see Schaufeli and
and has been widely used in both samples of high school students and De Witte, 2023). So far, only two studies have been published that
undergraduate university students (e.g., Madigan and Curran, 2021; have demonstrated the psychometric properties of the BAT-S to
Salanova et al., 2010; Salmela-Aro et al., 2022; Vizoso et al., 2019; Xie date. First, Romano et al. (2022), in a sample of 745 students from
et al., 2019). Despite the relevance that the MBI-SS has had for the two Italian public middle schools, report that the structure of four
study of burnout in academic settings, the conceptual, psychometric, first-order factors (i.e., exhaustion, mental distance, cognitive
and practical weaknesses of the MBI-GS –given their equivalencies– impairment, and emotional impairment) and 1 second-order factor
can be reasonably generalized to the use of the MBI-SS (for a (i.e., academic burnout) fits significantly better compared to a
systematic and meta-analytical review, see De Beer et al., 2024). series of alternative models (e.g., a unidimensional model).
Addressing the limitations of the MBI-GS, Schaufeli et al. (2020) Additionally, the authors report that both the composite and
developed the Burnout Assessment Tool (BAT), a new tool for dimension scores are significantly related to well-being, resilience,
individual and group assessment of burnout. For this purpose, they anxiety, and exhaustion indicators. Second, Popescu et al. (2023),
conducted interviews with 50 health professionals –who attended to in a sample of 399 Romanian undergraduate students, support the
burned-out people on a daily basis– using a dialectical method with second-order factor structure and describe significant relationships
deductive and inductive approaches. The content analysis of the with indicators of depression, anxiety, stress, psychosomatic
interviews revealed four core dimensions: exhaustion, mental distance, symptoms, prospective evaluation of future tasks, and coping
cognitive impairment, and emotional impairment. strategies. Hence, it seems relevant to continue investigating the
From this perspective, academic burnout describes those students psychometric properties of the BAT-S, also in other national and
who experience a severe loss of energy that results in feelings of both cultural contexts.
physical and mental fatigue (i.e., being exhausted); a strong reluctance The current research is unique as it aims to provide the first
or aversion to study, indifference, and cynicism (i.e., being mentally validation of the student version of the BAT in a Spanish-speaking
distanced); memory problems, attention and concentration deficits, context. So, this study fills a gap by examining the psychometric
and poor cognitive performance (i.e., cognitive impairment); and properties of the short 12-item version of the BAT-S in a sample of
intense emotional reactions such as anger or sadness and feeling Chilean undergraduate students following both within-network and
overwhelmed by one’s emotions (i.e., emotional impairment). between-network construct validity. The first refers to assessing
In this new conceptualization, exhaustion plays a central role in reliability, factor structure, and gender invariance, while the second
reducing the capacity to regulate cognitive and emotional processes refers to assessing the extent to which academic burnout is associated
and their subsequent deterioration. At the same time, mental distance with theoretically related constructs. More specifically, we use as a
is considered a counterproductive coping strategy that contributes to conceptual framework the Job Demands Resources (JD-R) model
the increase in exhaustion (Schaufeli and De Witte, 2023). (Bakker and Demerouti, 2017), which is one of the most applied
Consequently, students who experience high levels of burnout have frameworks in occupational health psychology for examining the
problems processing information and managing their emotions. In an relationship between employee well-being and its antecedents and
attempt to cope with these issues, they distance themselves outcomes (Bakker and Demerouti, 2017; De Beer et al., 2022b;
psychologically from their stressful academic activities, leads to Schaufeli and Taris, 2014), and has been successfully applied in the
negative consequences (e.g., non-fulfillment of commitments, academic context (e.g., Salmela-Aro et al., 2022; Salmela-Aro and
problems with peers, accumulation of academic load, poor academic Upadyaya, 2014; Zeijen et al., 2024). In this line, academic demands
performance), which, in their turn aggravate feeling of stress can be defined as the aspects of the studies that require sustained
and burnout. effort and are associated with certain physiological and psychological
On the one hand, the BAT produces a composite score, and, on costs, while academic resources can be defined as the aspects of the
the other hand also scores for each of the four symptom-dimensions. studies that have motivating potential, that are functional in
Hence, it has a hierarchical structure equivalent to a model of four achieving work goals, that regulate the impact of academic demands,
first-order factors (i.e., exhaustion, mental distance, cognitive and that stimulate learning and personal growth (Bakker et al., 2023).
impairment, and emotional impairment) and one higher-order factor In the present study we focus on study overload and teacher support,
(i.e., burnout), which is consistent with the notion of a burnout two constructs that have previously been considered as academic
syndrome (World Health Organization, 2019). Its psychometric demand and resource and have been shown to be related to academic
properties, both of the original (BAT-23), the short (BAT-12) and burnout (Lesener et al., 2020; Salmela-Aro et al., 2022; Zeijen
ultra-short (BAT-4) versions, have been demonstrated in various et al., 2024).
countries (e.g., Italy–Consiglio et al., 2021; Croatia–Tomas et al., 2023; Based on the background information presented, our hypotheses
South Africa–De Beer et al., 2022a; Greece–Androulakis et al., 2023; are as follows: (1) the abbreviated version of BAT-S will demonstrate
Norway–De Beer et al., 2023; Romania–Oprea et al., 2021; Japan– acceptable psychometric properties in a sample of Chilean
Sakakibara et al., 2020; Australia–Redelinghuys and Morgan, 2023; undergraduate university students; (2) academic demands will
Equator–Vinueza-Solórzano et al., 2021; Brazil–Sinval et al., 2022; be positively associated with academic burnout; and (3) academic
among others–Basinka et al., 2021; De Beer et al., 2020; resources will be negatively associated with academic burnout.

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Methods questionnaire during their regular class hours. The time taken to
answer the questionnaire was approximately 15 min.
Sample

The initial sample consisted of 474 Chilean undergraduate students. Statistical analyses
Following the recommendations of the literature on careless responding
(e.g., Ward and Meade, 2023), the final sample consisted of 461 Chilean All analyses were performed with JASP (2021) v 0.18.3 and Mplus
undergraduate students from the following programs: health (52.4%; v 8.2 (Muthén and Muthén, 1998) software. First, the distribution
n = 241), social sciences (38.1%; n = 176), engineering (5.4%; n = 25), characteristics of the variables were analyzed (mean, standard
and education (4.1%; n = 19). Of the total participants, 70.9% (n = 327) deviation, skewness, kurtosis, and Shapiro–Wilk test), as well as
identified themselves as female and 29.1% (n = 134) as male, with an gender differences (independent t-tests). Second, the internal
age range between 18 and 58 years (M = 22.4; SD = 4.34). structure of the BAT-S was analyzed by performing a confirmatory
factor analysis (CFA) with a weighted least square mean and variance-
adjusted (WLSMV) extraction method. The goodness of fit was
Instruments assessed by calculating the chi-square (χ2) and normalized χ2, the
root mean square error of approximation (RMSEA) with a 90%
The abbreviated version of the BAT-S was used (available at)1. This confidence interval (CI), the comparative fit index (CFI) and the
version includes 12 items that assess –using a Likert-type response standardized root mean residual (SRMR). The global fit indicators of
format with scores between 1 (never) and 5 (always)– the four the models were interpreted according to the guidelines proposed by
dimensions of academic burnout: exhaustion (3 items, e.g., “Due to my Hair et al. (2019). Third, the reliability of the scores was estimated with
studies, I feel mentally exhausted”), mental distance (3 items; e.g., “I the Cronbach’s alpha and McDonald’s omega indexes with 95%
struggle to find any enthusiasm for my studies”), cognitive impairment confidence interval (CI). Fourth, to establish the equivalence of the
(3 items; e.g., “When I am working on my studies, I have trouble staying BAT-S between students’ gender, a second-order multiple-group CFA
focused”), and emotional impairment (3 items; e.g., “I feel unable to was performed following the recommendations of Wang and Wang
control my emotions”). The adaptation to the usual conditions of the (2019). Changes in CFI of 0.010 or less (Chen, 2007; Cheung and
Chilean undergraduate students was carried out following the guidelines Rensvold, 2002; Dimitrov, 2010) were considered a criterion for
of the International Test Commission (2017) and the specialized determining whether measurement invariance was established.
literature (see Muñiz et al., 2013; Vallejo-Medina et al., 2017). Prior to Fourth, to examine criterion validity, a structural equation model
the data collection, the items were evaluated in a pilot study by a sample (SEM) was performed to evaluate the role of academic demands and
of undergraduate Chilean students (n = 10) who were asked to point out resources in academic burnout, assessed through BAT-S.
any difficulties associated with the comprehension of the items and the
response format. At this stage, no student expressed problems with the
wording of the items or with the item response format. Results
The teacher-student relationship scale (Martin et al., 2007) was
used to measure teacher support (which is considered an academic Descriptive analysis
resource). This scale has 4 items (e.g., “My teachers give me the help and
support I need”) and a Likert-type response format was used with scores Table 1 shows the descriptive statistics of the Spanish version of
ranging from 1 (strongly disagree) to 7 (strongly agree). Adequate BAT-S at the item level. The Shapiro–Wilk test showed that the items are
Cronbach alpha (α = 0.899) and McDonald’s omega (ω = 0.901) indices not normally distributed. Independent-sample t-tests revealed that –in
were obtained in the present study. As a measure of study overload accordance with meta-analytical studies (e.g., Purvanova and Muros,
(considered an academic demand), we use a self-constructed six-item 2010; Fiorilli et al., 2022)– female students (M = 2.959, SD = 0.694)
scale (e.g., “Currently, I have a heavy academic workload”) that assesses scored significantly higher than male (M = 2.808, SD = 0.741) students,
the perception of academic overload using a Likert-type response t (459) = 2.085, p < 0.050, d = 0.214, 95% CI (0.012, 0.415). However, the
format, with scores ranging from 1 (strongly disagree) to 7 (strongly effect size is small based on Cohen’s (1988) criterion.
agree). Adequate Cronbach alpha (α = 0.909) and McDonald’s omega
(ω = 0.910) indices were obtained in the present study.
Internal structure

Procedure Two models were specified to evaluate the internal structure of the
Spanish version of the BAT-S. The first model (M1) assumes that one
The data were collected in the context of a research project that latent factor is underlying all scale items, whereas Model 2 (M2)
sought to analyze the well-being levels of the Chilean university proposes a structure of four first-order factors (i.e., exhaustion, mental
population. The project was approved by the research ethics committee distance, cognitive impairment, and emotional impairment) and
of the host university. Participants voluntarily completed an online 1 second-order factor (i.e., academic burnout). The results show that
the one-factor solution does not obtain adequate fit indices and,
therefore, is not a good representation of the data collected (M1 in
Table 2), while the second-order factor solution obtains adequate fit
1 https://burnoutassessmenttool.be indices except for the RMSEA value (M2 in Table 2). Therefore,

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TABLE 1 Descriptive and reliability information at item level of BAT-S and factor loading resulting from confirmatory factor analysis.

Descriptive statistics Reliability statistics Factor loadings

M (SD) S K SW ω if item α if item CHI EX MD CI EI SE
is is
dropped dropped
1. Due to my
3.776
studies, I feel -0.472 -0.042 0.872* 0.862 0.858 0.601 0.862* 0.025
(0.894)
mentally exhausted

2. After a day of
working on my
3.466
study, I find it hard -0.223 -0.851 0.900* 0.863 0.860 0.575 0.812* 0.024
(1.115)
to recover my
energy

3. While working
on my studies, I feel 3.504
-0.287 -0.537 0.901* 0.865 0.862 0.543 0.811* 0.022
physically (1.063)
exhausted

4. I struggle to find
3.133
any enthusiasm for 0.006 -0.843 0.912* 0.863 0.859 0.610 0.802* 0.027
(1.189)
my studies

5. I feel a strong
2.487
aversion toward my 0.347 -0.442 0.896* 0.863 0.859 0.620 0.820* 0.029
(1.090)
studies

6. I’m cynical about

2.013
what my study 0.978 0.154 0.809* 0.881 0.881 0.239 0.361* 0.050
(1.141)
means to others

7. When
I am working on
3.169
my studies, I have 0.099 -0.733 0.908* 0.861 0.857 0.623 0.870* 0.019
(1.102)
trouble staying
focused

8. When
I am working on
3.468
my studies. I have -0.118 -0.755 0.900* 0.865 0.862 0.542 0.794* 0.022
(1.042)
trouble
concentrating

9. I make mistakes
while working on
3.019
my studies because 0.154 -0.854 0.907* 0.862 0.859 0.591 0.752* 0.025
(1.118)
I have my mind on
other things

10. I feel unable to

2.502
control my 0.475 -0.632 0.893* 0.860 0.857 0.633 0.844* 0.023
(1.171)
emotions

11. I do not
recognize myself in 2.308
0.547 -0.556 0.877* 0.862 0.859 0.610 0.824* 0.022
the way I react (1.142)
emotionally

12. I may overreact 2.278

0.657 -0.544 0.862* 0.867 0.863 0.527 0.696* 0.030
unintentionally. (1.198)

* p < 0.001; M, mean; SD, standard deviation; S, skewness; K, kurtosis; SW, Shapiro–Wilk test; CHI, corrected homogeneity index; EX, exhaustion; MD, mental distance; CI, cognitive
impairment; EI, emotional impairment; SE, standard error.

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we examined the modification indices and proceeded to covary the RMSEA = 0.061, 90% CI [0.055, 0.067]; SRMR = 0.061. Figure 1 shows
measurement error of items 7 and 9, which both refer to the difficulty that, as expected, teacher support and study overload are significantly
in staying focused and correspond to the cognitive impairment negatively and positively related academic burnout, respectively.
dimension (see Table 1). As a result, the re-specified second-order Furthermore, according to Pearson’s correlation coefficient,
factor solution (M4 in Table 2) demonstrates an adequate fit to the teacher support and study overload are significantly related to
data. Table 1 shows the factor loadings obtained for the M4. academic burnout and its dimensions (see Table 3).

Reliability of the scores Discussion

The Spanish version of BAT-S, based on Kalkbrenner (2021), This brief report provides empirical evidence about the
shows adequate internal consistency both for the global score psychometric properties of the abbreviated version of the Burnout
(ω = 0.874, 95% CI [0.856, 0.891]; α = 0.870, 95% CI [0.852, 0.887]) Assessment Tool for Students (BAT-S) in a sample of Chilean
and for each of its dimensions: exhaustion (ω = 0.828, 95% CI [0.800, undergraduate students.
0.855]; α = 0.823, 95% CI [0.794, 0.849]), mental distance (ω = 0.689, The obtained results show that the BAT-S performed well in a
95% CI [0.640, 0.738]; α = 0.652, 95% CI [0.593, 0.704]), cognitive sample of Chilean undergraduate students showing acceptable
impairment (ω = 0.804, 95% CI [0.773, 0.835]; α = 0.798, 95% CI reliability, which is consistent with previous studies in both academic
[0.763, 0.828]), and emotional impairment (ω = 0.795, 95% CI [0.762, (e.g., Popescu et al., 2023; Romano et al., 2022) and organizational
0.827]; α = 0.792, 95% CI [0.757, 0.823]). settings (e.g., Schaufeli and De Witte, 2023; Vinueza-Solórzano et al.,
2021). The internal structure of the Spanish version of the BAT-S is
adequately explained by a model of four first-order factors (i.e.,
Measurement invariance exhaustion, mental distance, cognitive impairment, and emotional
impairment) and 1 second-order factor (i.e., academic burnout),
A second-order multiple-group CFA was performed to assess which is compatible with the notion of a burnout syndrome. Moreover,
whether the structure of the BAT-S is equivalent according to the this second-order model proves to be invariant to student’s gender,
gender of the students. Following Wang and Wang (2019), the first which is also consistent with previous studies (e.g., De Beer et al., 2020;
step was to verify the configural invariance of the second-order model Schaufeli et al., 2020; Schaufeli and De Witte, 2023; Sinval et al., 2022).
(M8 in Table 2). Next, three levels of equivalence (i.e., configural, In addition, criterion validity of the BAT-S was verified using the JD-R
metric, scalar) of the first-order factors were verified (M5, M6, M7 in model, with an adequate fit of the proposed model and significant
Table 2). Finally, the metric invariance of the second-order model was effects on academic burnout of both academic resources and demands,
verified (M9 in Table 2). All model fits were adequate, and the consistent with previous studies (e.g., De Beer et al., 2022b; Lesener
differences in the CFI met the established criteria, supporting the et al., 2020; Salmela-Aro et al., 2022; Zeijen et al., 2024).
equivalence of the second-order structure regarding student gender. This study’s unique strength lies in its pioneering analysis of the
psychometric properties of the BAT-S in a Spanish-speaking country,
a novel and unexplored area of research. The findings of this research
Criterion validity contribute to the initiation of a future research agenda related to
academic burnout, starting with the conceptualization of BAT in
The SEM, based on previously described M4 model, obtains adequate countries where Spanish is an official language. Furthermore, our
fit indices: χ2 (201) = 543.509, p < 0.05; CFI = 0.938; TLI = 0.928; results suggest that the BAT-S may be adequately integrated into the

TABLE 2 Fit indexes for the single-group and multiple-group CFA of the BAT-S.

X2 df p X2/df RMSEA 90% CI CFI TLI SRMR CMs ΔCFI

Single-group CFA
M1 one factor 895.061 54 0.000 16.575 0.184 [0.173, 0.194] 0.827 0.789 0.082 - -

M2 second order 218.124 50 0.000 5.297 0.085 [0.074, 0.097] 0.965 0.954 0.039 - -

M3 one factor re-specified 894.876 53 0.000 16.884 0.186 [0.175, 0.196] 0.827 0.784 0.081 - -

M4 second order re-specified 170.296 49 0.000 3.475 0.073 [0.061, 0.085] 0.975 0.966 0.035 - -

Multiple-group CFA
M5 Configural invariance 121.147 94 0.031 1.288 0.035 [0.011, 0.052] 0.982 0.975 0.040 - -

M6 Metric invariance 128.033 102 0.041 1.255 0.033 [0.007, 0.050] 0.983 0.978 0.044 M5-M6 0.001

M7 Scalar invariance 152.290 110 0.004 1.384 0.041 [0.023, 0.056] 0.973 0.967 0.050 M6-M7 0.010

M8 Configural invariance * 128.961 102 0.036 1.264 0.034 [0.009, 0.051] 0.983 0.977 0.042 - -

M9 Metric invariance * 134.773 109 0.047 1.236 0.032 [0.004, 0.049] 0.983 0.980 0.047 M8-M9 0.000

*, second order invariance; χ2, Chi-square; df, degree of freedom; RMSEA, root mean square error of approximation; 90% CI, confidence interval; CFI, comparative fit index.
TLI, Tucker-Lewis index; SRMR, standardized root mean square residual; CMs, comparisons between models.

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FIGURE 1
Graphical representation of the structural equation model between academic burnout, academic resources, and academic demands.

TABLE 3 Correlation analysis.

Study Teacher support Exhaustion Mental Cognitive Emotional

overload distance impairment impairment
Exhaustion 0.462* [0.531, 0.387] -0.315* [− 0.230, − 0.395] -

Mental distance 0.161* [0.249, 0.071] -0.355* [− 0.272, − 0.432] 0.410* [0.331, 0.483] -

Cognitive
0.290* [0.371, 0.204] -0.289* [− 0.203, − 0.371] 0.440* [0.363, 0.511] 0.519* [0.449, 0.583] -
impairment

Emotional
0.354* [0.432, 0.272] -0.297* [− 0.212, − 0.378] 0.506* [0.435, 0.571] 0.458* [0.382, 0.527] 0.494* [0.422, 0.560] -
impairment

Academic burnout 0.409* [0.482, 0.330] -0.403* [− 0.324, − 0.477] 753.* [0.711, 0.790] 0.761* [0.719, 0.797] 0.789* [0.752, 0.822] 0.803* [0.768, 0.833]
* = p < 0.01; [] = 95% CI.

JD-R model, which –as far as we know– has not been previously done other academic demands (e.g., time pressure), academic resources
in academic contexts. However, some limitations should be considered. (e.g., academic PsyCap), and academic outcomes (e.g., achievement)
First, the results should be cautiously generalized since our sampling under de Study Demands-Resources theory (e.g., Bakker and
does not represent Chilean students. Second, the data were collected Mostert, 2024).
using a cross-sectional self-reported survey instrument and may
be prone to social desirability bias. Third, modification indices
correlated two errors and improved the BAT-S′ fit. Notwithstanding Data availability statement
these limitations, this study provides first evidence for using a brief tool
that overcomes the theoretical and psychometric limitations of The raw data supporting the conclusions of this article will
instruments traditionally used to measure academic burnout. Finally, be made available by the authors, without undue reservation.
according to the available literature from the organizational context,
future research may consider analyzing cross-national representative
samples (e.g., De Beer et al., 2020), establishing cut-off points for severe Ethics statement
academic burnout (e.g., Schaufeli et al., 2023), to deepen the
psychometric properties using alternative models (e.g., ESEM, Rasch The studies involving humans were approved by Comité Ético-
analysis, and item-level analysis), and analyze the relationship with Científico/Universidad de Tarapacá (CEC-UTA). The studies were

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Carmona-Halty et al. 10.3389/fpsyg.2024.1434412

conducted in accordance with the local legislation and institutional Proyecto de Fortalecimiento de Grupos de Investigación #
requirements. The participants provided their written informed 3798–24.
consent to participate in this study.

Conflict of interest
Author contributions
The authors declare that the research was conducted in the
MC-H: Writing – review & editing, Writing – original draft. absence of any commercial or financial relationships that could
KA-C: Writing – review & editing, Writing – original draft. CS-G: be construed as a potential conflict of interest.
Writing – original draft. GS-P: Writing – original draft. WS: Writing
– review & editing.
Publisher’s note
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