Validity and Reliability of a Japanese Version of the Oldenburg Burnout Inventory–Medical Student: A Study on Students in Clinical Clerkships

Validity and Reliability of a Japanese Version of the Oldenburg Burnout Inventory–Medical Student: A Study on Students in Clinical Clerkships | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validity and Reliability of a Japanese Version of the Oldenburg Burnout Inventory–Medical Student: A Study on Students in Clinical Clerkships Takafumi Watanabe, Ichiro M. Omori, Tomohiro Nakaguchi, Nao Shiraisi, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7717852/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Burnout, characterised by exhaustion, disengagement, and diminished professional efficacy, represents a significant concern in medical education, particularly during clinical training. Although the construct has been extensively studied worldwide, a validated Japanese version of the Oldenburg Burnout Inventory for Medical Students (OLBI-MS) was previously unavailable. Methods Following the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN), we translated and culturally adapted the OLBI-MS into Japanese. A cross-sectional survey was conducted among 195 fifth- and sixth-year medical students during psychiatry clinical clerkships at two Japanese university hospitals. Participants completed an online survey comprising the Japanese OLBI-MS and other established instruments: the Maslach Burnout Inventory–General Survey, Work-related Acceptance and Action Questionnaire, Valuing Questionnaire, Perceived Devaluation–Discrimination Scale, Patient Health Questionnaire-9, and a mistreatment measure. We examined internal consistency (Cronbach’s alpha and McDonald’s omega), test–retest reliability (intraclass correlation), and structural validity (confirmatory and exploratory factor analyses, and bifactor modelling). Measurement invariance by gender and hypothesis-based construct validity (via Pearson correlations) were also assessed. Results The original 16-item version exhibited poor model fit. The refined 11-item version demonstrated a robust two-factor structure (Exhaustion and Disengagement) and acceptable fit across exploratory and bifactor models. The instrument showed strong internal consistency (alpha and omega ≥ 0.77), high test–retest reliability (ICC ≥ 0.80), and confirmed gender invariance. Construct validity was supported through expected correlations with related psychological measures. Conclusions The Japanese OLBI-MS is a psychometrically sound and culturally suitable instrument for assessing burnout among medical students in clinical training. The 11-item version offers a practical tool for ongoing assessment and may facilitate cross-cultural and longitudinal research on medical student well-being. medical student burnout clinical clerkship Oldenburg Burnout Inventory reliability validity Background Burnout is a work-related condition defined by energy depletion, mental detachment from one's work, negative or cynical attitudes, and reduced professional efficacy [ 1 ]. Originally described by Freudenberger in the 1970s, it has since been conceptualised through various psychological frameworks and evaluated using psychometric tools [ 2 ]. Maslach and colleagues outlined three core dimensions—exhaustion, depersonalisation (later termed cynicism), and diminished personal accomplishment (later termed professional efficacy)—based on empirical studies [ 3 , 4 ]. Several theoretical models have attempted to explain how burnout develops. Early frameworks proposed a sequential model: high demands lead to exhaustion, which in turn results in cynicism and reduced professional efficacy [ 4 ]. More recent theories, including the Conservation of Resources (COR) theory and the Job Demands–Resources (JD–R) model, focus on the imbalance between demands and available resources as central to burnout [ 5 , 6 ]. Burnout prevalence rises sharply during the clinical year of medical school [ 7 ]. A recent study found that 28.8% of medical students reported high exhaustion, 14.6% high cynicism, 87.6% low professional efficacy, and 34.1% severe overall burnout [ 8 ]. Associated risk factors include symptoms of depression and anxiety [ 9 ], lower psychological flexibility—defined as the ability to pursue valued goals without avoiding uncomfortable internal experiences [ 10 , 11 ], —mental health stigma [ 12 ], and experiences of mistreatment during clinical training [ 13 ]. The Maslach Burnout Inventory–General Survey (MBI-GS) is widely used but has known limitations. Its Professional Efficacy (PE) subscale is positively worded, in contrast to the negatively worded Exhaustion (EX) and Cynicism (CY) subscales, increasing the risk of artificial factor structures. Furthermore, the PE subscale often shows inconsistent associations with other burnout domains and related outcomes [ 14 , 15 ]. The Oldenburg Burnout Inventory (OLBI), developed using the JD–R model, addresses these limitations by assessing only two dimensions—Exhaustion and Disengagement—with both positively and negatively worded items [ 6 ]. A student-specific version (OLBI-MS) is used in major U.S. surveys [ 16 , 17 ], and numerous studies [ 13 , 18 – 20 ]. To date, no validated Japanese version of the OLBI-MS exists. While a version has been validated for nurses [ 21 ], medical students face qualitatively distinct challenges as learners and novice clinicians, including role transition, identity formation, and ethical stressors. Japan’s medical education system is shifting from passive observation to team-based clinical clerkships [ 22 ], heightening the need for context-specific assessment tools. Our pilot study of an ACT-based intervention also highlighted the need for brief, reliable, and culturally validated burnout measures [ 11 ]. Objectives and Hypotheses This study aimed to develop a Japanese version of the OLBI-MS and evaluate its psychometric properties among medical students in clinical clerkships. We hypothesised that the original two-factor structure (Disengagement and Exhaustion) would be replicated and that the instrument would demonstrate acceptable reliability and construct validity. Specifically: Hypothesis 1 Disengagement would correlate positively with cynicism (CY) from the MBI-GS. Hypothesis 2 Exhaustion would correlate positively with exhaustion (EX) from the MBI-GS. Hypothesis 3 Both factors would correlate negatively with professional efficacy (PE). Hypotheses 4–6 In terms of psychological flexibility, both factors would (4) be negatively associated with the ability to engage in work-related activities without avoiding unpleasant internal experiences, (5) negatively with perceived progress toward personally important values, and (6) positively associated with perceived barriers to value-consistent behaviour. Hypothesis 7 Both factors would be positively associated with depressive symptoms. Hypothesis 8 Both factors would be positively associated with perceived mental health stigma. Hypothesis 9 Both factors would be positively associated with experiences of mistreatment in clinical contexts. Methods Study design This multicentre, cross-sectional study was conducted in accordance with classical test theory and the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) checklist for evaluating the measurement properties of patient-reported outcome measures [ 23 ]. Based on COSMIN guidelines, a minimum of 100 participants is required for testing construct validity, internal consistency, and reliability. Additionally, a heuristic recommendation of 7 participants per item suggests 112 cases for the 16-item OLBI-MS. Anticipating potential invalid responses, we set the target sample size at 160 by multiplying the number of items by 10. An a priori power analysis was conducted using G*Power 3.1 [ 24 ] to estimate the required sample size for confirmatory factor analysis (CFA). Assuming a two-factor model with 16 observed variables (approx. 100 degrees of freedom), a medium effect size (Cohen’s w = 0.2), alpha = 0.05, and power = 0.80, the minimum sample size was estimated at 240. While our final sample size of 195 fell slightly below this threshold, it remained within the acceptable range for structural equation modelling under realistic constraints [ 25 ]. Setting and participants The participants were 87 fifth- and 119 sixth-year medical students undergoing clinical clerkship in psychiatry at Nagoya City University Hospital from 19 December 2022 to 24 July 2023 and at the University of Fukui Hospital from 10 January 2023 to 22 December 2023 in Japan (where the school year begins in April). Both hospitals are affiliated with medium-sized university medical schools and train Japanese physicians. These hospitals were selected for their clinical clerkship programs, which allow students to participate in clinical training as part of a team. This study was approved by the Institutional Review Board of Nagoya City University Graduate School of Medical Sciences and registered with the University Hospital Medical Information (UMIN) Clinical Trials Registry (Contact No. UMIN000048572), registered on 4 August 2022, before starting. Prior to the commencement of their psychiatric clinical clerkship, all participants received written and verbal information about the study. They were assured that non-participation would not affect their grades and that their privacy would be protected. Participants were given ample time to decide whether to participate. Those who agreed accessed the Google Form online and checked the consent box in the instructions. This procedure constituted informed consent, and the study adhered to the Declaration of Helsinki. Data collection The data were collected anonymously through an online survey using Google Forms. Respondents were emailed within 7 to 14 days of completing the first survey to respond again to the OLBI-MS for retest reliability validation. Participants who returned both surveys were incentivised with a 500-yen email gift certificate. Instruments The instruments used in the study included the OLBI-MS, MBI-GS, Work-related Acceptance and Action Questionnaire (WAAQ), Valuing Questionnaire (VQ), Perceived Devaluation-Discrimination Scale (PDDS), and Patient Health Questionnaire-9 (PHQ-9). Data were collected on the frequency of mistreatment by faculty and residents, as well as various demographic characteristics such as sex, age, alcohol consumption (more than three per week), participation in extracurricular activities, presence of housemates, having a part-time job, history of enrolment in other faculties, presence of a spouse, and parenting status. Oldenburg Burnout Inventory-Medical Student (OLBI-MS) The OLBI-MS is unique because it consists of disengagement and exhaustion, including mental and physical aspects, with both positive and negative wording for each factor’s items in psychometric measures. It uses a 4-point Likert scale ranging from 1 (strongly agree) to 4 (strongly disagree), with eight items for each factor. If needed, items are reverse scored, and an average score is calculated for each factor. Higher scores indicate a higher sense of disengagement and exhaustion [ 14 , 26 ]. Disengagement is described as distancing or negative attitude toward work. Exhaustion refers to cognitive, affective, and physical fatigue resulting from work demands. The average total scores for the disengagement and exhaustion subscales are also provided [ 27 ]. Translation of the OLBI-MS into Japanese The scale was translated into Japanese following the COSMIN guidelines. Two native Japanese psychiatrists, both with sufficient knowledge of English and one of them with knowledge and experience in burnout in medical professionals and medical students independently translated the original OLBI-MS into Japanese. One of them had experience in burnout among medical professionals and students. Permission was obtained from Dr. Evangelia Demerouti, the original developer of the OLBI-MS. A committee of three native Japanese psychiatrists, with English proficiency and experience in burnout research, reviewed the translations and created a preliminary Japanese version. This version was then back-translated into English by a psychiatrist and a language expert unfamiliar with the original OLBI-MS. The translations were reviewed, merged, and revised by the committee. Dr. Demerouti confirmed the content similarity with the original version. Eight medical students in clinical training received the preliminary Japanese version of the OLBI-MS to check for inappropriate expressions, suggesting alternatives that were incorporated to finalise the Japanese version of the OLBI-MS. Maslach Burnout Inventory-General Survey (MBI-GS) The MBI includes the MBI-GS (student version) for university students. Medical students work as interpersonal helpers during clinical rotations. As the OLBI-MS has shown structural validity with the MBI-GS, it was used. The scale has 16 self-administered items [ 4 ] rated on a 7-point Likert scale from 0 (not at all) to 6 (every day). It includes three factors: EX, CY, and PE, with average scores for each factor calculated. The reliability and validity of the Japanese version have been confirmed [ 28 ]. High exhaustion is defined as EX ≥ 3.2, high cynicism as CY ≥ 2.2, and low job efficacy as PE ≤ 4.0 [ 4 ]. Showing one or more symptoms of burnout in addition to fatigue is a more appropriate indicator of severe burnout in the general population. This study also adopted the additional criterion of ‘exhaution + 1’ [ 29 ]. Work-related Acceptance and Action Questionnaire (WAAQ) The WAAQ assesses psychological flexibility in the work environment. Each item is rated on a scale of 1–7, with a total possible score of 7–49. Higher scores indicate better ability to manage unpleasant thoughts, feelings, impulses, and sensations at work without avoidance. Both the original and Japanese versions of the WAAQ have been proven to be reliable and valid [ 30 , 31 ].. Avoiding unpleasant internal experiences has been linked to burnout [ 10 ]. Valuing Questionnaire (VQ) The VQ consists of 10 items related to value-directed behaviours. Two subscales measure progress (VQ-P) and obstruction (VQ-O). Each factor includes five items rated on a scale of 0 to 6, totalling a score of 0 to 30. VQ-O is correlated with the Acceptance and Action Questionnaire-2 (AAQ-2), which assesses psychological inflexibility. The reliability and validity of the original version [ 32 ] and the Japanese version [ 33 ] have been documented. Decreased value-directed behaviours have been linked to burnout [ 10 ]. Patient Health Questionnaire-9 (PHQ-9) The PHQ is a self-administered questionnaire used by primary care physicians to diagnose and assess eight types of mental disorders [ 34 ]. The PHQ-9 is a subset of the questionnaire focused on major depressive disorder. It is scored on a 9-item, 4-point Likert scale, with higher scores reflecting most severe depressive symptoms. The original version, as well as the Japanese version, have been shown to be reliable and valid [ 35 , 36 ]. Depression and burnout are shown to be positively correlated [ 10 , 37 ]. Perceived Devaluation-Discrimination Scale (PDDS) The PDDS is a 12-item scale that assesses stigma toward individuals who have undergone psychiatric treatment. It is aimed at the general public, patients, and their families. The descriptive wording of the items helps prevent bias in responses. Previous research has shown a correlation between burnout and stigma toward mental illness [ 12 , 38 ], with higher burnout scores predicting increased stigma. The original PDDS version rates each item on a 6-point scale, giving a total score between 6 and 72. A higher total score suggests greater perceived stigma [ 39 , 40 ]. The Japanese version uses a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with 4 points for each item. The total score ranges from 4 to 48, and its reliability and validity have been verified [ 41 ]. Frequency of mistreatment by faculty or residents The question used by Cook et al. to assess mistreatment frequency in medical school was: ‘Have you ever been mistreated by a faculty member or resident in medical school?’ Responses were collected using a 4-point Likert scale with options including: ‘never’, ‘once or twice’, ‘several times’, and ‘many times’ [ 42 ]. This is relevant due to reported associations between mistreatment and burnout in medical training [ 13 , 42 , 43 ]. Statistical analysis All statistical analyses were conducted in accordance with the COSMIN checklist [ 23 ], using a two-tailed significance level of 0.05. CFA was performed using Mplus version 8.8, while all other analyses were conducted using R version 4.4.1, employing the relevant packages: lavaan, semTools, psych, irr, and GPArotation. Prior to analysis, reverse-coded items were rescored to ensure consistency in item direction. The OLBI-MS was developed using a reflective model. After collecting data, several statistical analyses were carried out to confirm the validity and reliability of the OLBI-MS. Internal consistency was assessed using Cronbach’s alpha and McDonald’s omega, calculated with the semTools and psych packages in R [ 44 , 45 ]. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC) based on a two-way random effects model with absolute agreement, implemented using the irr package. An ICC of ≥ 0.70 was considered acceptable [ 45 ]. CFA was utilised using Mplus to evaluate structural validity, assuming a 2-factor model corresponding to the disengagement and exhaustion factors. The model fit indices used were standardised root mean square residuals (SRMR), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A good fit was indicated by χ2/df ≤ 5 [ 46 ], CFI ≥ 0.95 [ 47 , 48 ], SRMR ≤ 0.08, and RMSEA ≤ 0.06 [ 48 , 49 ]. If the CFA model showed poor fit, exploratory factor analysis (EFA) was conducted using the psych and GPArotation packages. Sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity. The number of factors was determined using the eigenvalue > 1 criterion, scree plot, parallel analysis [ 50 ], and minimum average partial (MAP) test [ 51 ]. Maximum likelihood extraction with oblimin rotation was applied. Items with low factor loadings (< 0.50) or low inter-item correlations (< 0.60) were considered for removal during refinement. Given that the OLBI-MS includes both positively and negatively worded items, a bifactor model was estimated using lavaan to assess method effects. The model specified one general burnout factor, two group-specific factors (Disengagement and Exhaustion), and one method factor representing variance due to item wording. All latent factors were modelled as orthogonal. Fit indices were compared to the standard two-factor model [ 52 , 53 ]. Measurement invariance across gender (male vs. female) was tested using multiple-group CFA in lavaan. Invariance was assessed hierarchically via three nested models: (1) configural (unconstrained), (2) metric (constraining factor loadings), and (3) scalar (constraining loadings and intercepts). Model fit was evaluated using CFI, RMSEA, and SRMR. Invariance was judged using chi-square difference tests and accepted cut-off values: ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 [ 54 , 55 ]. To assess construct validity, Pearson correlation coefficients were calculated between the OLBI-MS and theoretically related constructs (MBI-GS, WAAQ, VQ, PHQ-9, PDDS, and mistreatment frequency). Correlation strength was interpreted using thresholds of ± 0.50 (strong), ± 0.30 (moderate), and ± 0.10 (weak) [ 56 , 57 ]. These analyses were used to test nine predefined hypotheses (Hypothesis 1 – Hypothesis 9 ). Results Sociodemographic characteristics Of the 206 medical students invited to participate, 195 completed the survey (response rate = 94.7%). The sample comprised 136 males (69.7%) and 59 females (30.3%), with a mean age of 24.45 years (SD = 3.00). Table 1 summarises the demographic characteristics and descriptive statistics for each instrument. Table 1 Clinical Condition and Demographic Characteristics of the Participants Total sample (n = 195) Number (%) Sex (women) 59 (30.3) Sex (men) 136 (69.7) High exhaustion 80 (41.0) High cynicism 70 (35.9) Low professional efficacy 174 (89.2) Severe burnout 73 (37.4) Alcohol consumption more than thrice per week 21 (10.8) Participation in extracurricular activity 120 (61.5) Presence of housemates 54 (27.7) Parenting 1 (0.5) History of enrolment in other faculties 21 (10.8) Having a part-time job 136 (69.7) Spouse 6 (3.1) University 1 (Nagoya City) 82 (42.1) University 2 (Fukui) 113 (57.9) Mean (SD) Age 24.45 (3.00) Frequency of mistreatment by faculty or residents 1.82 (0.84) Cut-off scores for each factor of burnout were as follows: MBI-GS-CY ≥ 2.2, MBI-GS-EX ≥ 3.2, and MBI-GS-PE ≤ 4.0, indicating high cynicism, high exhaustion, and low professional efficacy, respectively. Severe burnout was defined as MBI-GS-EX ≥ 3.2 and either MBI-GS-CY ≥ 2.2 or MBI-GS-PE ≤ 4.0. SD = Standard deviation. [Table 1 near here] Structural validity was conducted after reverse scoring to examine whether the Japanese version of the OLBI-MS preserved the original two-factor structure (Table 2 ). The model fit indices—SRMR = 0.114, CFI = 0.717, and RMSEA = 0.115—indicated poor fit. Consequently, two models were specified: Model 1 included all 16 items, whereas Model 2 retained 11 items (D1, D2, D3, D4, D6, D8, E2, E4, E5, E7, and E8), selected based on factor loadings (≥ 0.5) and inter-item correlations (≥ 0.6). Table 2 Confirmatory Factor Analysis Results with All OLBI-MS Items (16 items) Item Subscale: Name in manuscript Factor loading Item-total correlation Mean (SD) R square I always find new and interesting aspects in my medical school work. D1: Interest 0.559 0.613 1.790 (0.567) 0.313 It happens more and more often that I talk about my medical school work in a negative way. D2: Talk 0.757 0.736 2.410 (0.764) 0.573 Lately, I tend to think less at medical school and do my medical school work almost mechanically. D3: Think 0.626 0.661 2.533 (0.768) 0.392 I find my medical school work to be a positive challenge. D4: Challenge 0.671 0.717 1.872 (0.680) 0.450 Over time, one can become disconnected from medical school work. D5: Disconnected 0.459 0.545 2.513 (0.699) 0.211 Sometimes I feel sickened by my medical school work. D6: Sickened 0.767 0.765 2.605 (0.782) 0.588 The study of medicine is the only thing that I can imagine myself doing. D7: Image 0.087 0.347 2.451 (0.975) 0.008 I feel more and more engaged in my medical school work. D8: Engaged 0.609 0.674 2.359 (0.692) 0.371 Usually, I can manage the amount of my medical school work well. E1: Manage 0.310 0.529 2.303 (0.678) 0.096 After a day of medical school work, I tend to need more time than in the past in order to relax and feel better. E2: Relax 0.556 0.615 2.205 (0.824) 0.309 I can tolerate the pressure of my medical school work very well. E3: Pressure 0.259 0.485 1.990 (0.681) 0.067 During my medical school work, I often feel emotionally drained. E4: Drained 0.724 0.700 2.713 (0.799) 0.525 After a day of medical school, I have enough energy for leisure activities. E5: Leisure 0.441 0.625 2.174 (0.787) 0.195 When I am at medical school, I usually feel energized. E6: Energised 0.370 0.444 2.410 (0.708) 0.137 After a day of medical school, 1 feel worn out and weary. E7: Weary 0.595 0.658 2.590 (0.729) 0.354 There are days when I feel tired before I arrive at medical school. E8: Tired 0.730 0.674 2.687 (0.725) 0.533 Italic font indicates reversed items. Bold font indicates items adopted in the revised model. D = Disengagement, E = Exhaustion, SD = Standard deviation, OLBI-MS = Oldenburg Burnout Inventory–Medical Student [Table 2 near here] EFA was subsequently performed. The data met suitability criteria with a KMO value of 0.84 and significant results on Bartlett’s test for both Model 1 (χ² = 11,019.039, df = 120, p < 0.001) and Model 2 (χ² = 683.857, df = 55, p < 0.001). Based on the Kaiser criterion, parallel analysis, and minimum average partial correlation, a two- or three-factor solution was plausible for Model 1, and a two-factor solution for Model 2 (Supplementary Figs. 1 and 2; see Additional files 1 and 2).. Maximum likelihood extraction with oblimin rotation was used in both models (Table 3 ). Table 3 Results of the Exploratory Factor Analyses of the Original (16 Items) and Revised Measures (11 Items) Model Model 1 (16 items) Model 2 (11 items) Factor 2-factor solution 3-factor solution 2-factor solution 1 2 1 2 3 1 2 D1 (Interest) 0.600 -0.066 0.596 -0.027 -0.136 0.614 -0.091 D2 (Talk) 0.671 0.119 0.680 0.173 -0.199 0.727 0.083 D3 (Think) 0.602 0.002 0.596 0.041 -0.105 0.648 -0.040 D4 (Challenge) 0.756 -0.088 0.739 -0.061 -0.008 0.746 -0.107 D5 (Disconnected) 0.294 0.334 0.302 0.352 -0.094 - - D6 (Sickened) 0.624 0.228 0.632 0.275 -0.171 0.678 0.189 D7 (Imagine) 0.105 0.006 0.101 -0.009 0.084 - - D8 (Engaged) 0.697 -0.009 0.695 -0.036 0.271 0.598 0.011 E1 (Manage) 0.203 0.175 0.174 0.115 0.577 - - E2 (Relax) -0.004 0.594 0.002 0.613 -0.076 0.034 0.579 E3 (Pressure) 0.180 0.155 0.153 0.107 0.476 - - E4 (Drained) 0.074 0.712 0.086 0.703 -0.002 0.073 0.725 E5 (Leisure) -0.140 0.530 -0.164 0.498 0.411 -0.195 0.558 E6 (Energised) 0.785 -0.061 0.791 -0.090 0.258 - - E7 (Weary) -0.130 0.735 -0.133 0.720 0.121 -0.106 0.699 E8 (Tired) 0.262 0.585 0.272 0.589 -0.042 0.269 0.593 Note. Exploratory factor analysis was conducted using the maximum likelihood method with oblimin rotation. Factor pattern coefficients above 0.500 are in bold. Scree plots, parallel analysis, and minimum average partial correlation indicated that a 2-factor or 3-factor solution and a 2-factor solution could explain the data for the original and revised measures, respectively. [Table 3 near here] In Model 1, the first and second factors corresponded to disengagement and exhaustion, respectively, while a third factor emerged with only one strong loading (E1). Items D5, D7, and E3 did not load substantially on any factor, and E6 loaded more strongly on disengagement than on exhaustion. In contrast, all items in Model 2 showed strong, exclusive loadings (≥ 0.55) on their respective factors. Fit statistics (Table 4 ) supported the three-factor solution for Model 1 and the two-factor solution for Model 2. The 11-item OLBI-MS (Model 2) was selected for further analysis due to its clearer structure and superior model fit relative to the full 16-item version (see Supplementary Tables 1 and 2 in Additional files 3 and 4). Table 4 Fit Statistics for Different OLBI-MS Measurement Models Model Factor χ 2 (df) CFI RMSEA RMSEA 90% CI SRMR Model 1 (16 items) 2 203.431 (89) 0.878 0.081 0.067 0.096 0.060 3 101.471 (75) 0.972 0.043 0.017 0.062 0.033 Model 2 (11 items) 2 48.863 (34) 0.977 0.047 0.005 0.075 0.032 CFI = Comparative fit index, RMSEA = Root mean squared error of approximation, SRMR = Standardised root mean square residual, OLBI-MS = Oldenburg Burnout Inventory–Medical Student, CI = Confidence interval. [Table 4 near here] Bifactor Model and Method Effects A bifactor model was estimated using the 11-item OLBI-MS to assess potential method effects from reverse-coded items. The model included one general factor (g), two group-specific factors (Disengagement and Exhaustion), and one orthogonal method factor. Fit indices indicated good fit: χ²(26) = 37.16, p = 0.072, CFI = 0.983, TLI = 0.964, RMSEA = 0.047, SRMR = 0.037. Several items loaded significantly on both content and method factors, indicating that the bifactor model effectively accounted for method variance. Comparison with the standard two-factor model indicated improved fit (Table 5 ). Table 5 Comparison of Model Fit Indices Between the Two-Factor Model and the Bifactor Model of the 11-Item OLBI-MS Model χ 2 (df) AIC BIC CFI RMSEA RMSEA 90% CI SRMR Two-factor 92.40 (43) 4186.99 4262.27 0.924 0.075 0.055 0.098 0.078 Bifactor 37.156 (26) 4165.8 4296.7 0.983 0.047 0.000 0.079 0.037 AIC = Akaike information criterion, BIC = Bayesian information criterion, CFI = Comparative fit index, RMSEA = Root mean squared error of approximation, SRMR = Standardised root mean square residual, OLBI-MS = Oldenburg Burnout Inventory–Medical Student, CI = Confidence interval. [Table 5 near here] Measurement Invariance across Gender Multi-group CFA assessed measurement invariance for the 11-item OLBI-MS across gender. The configural model showed acceptable fit (χ²(86) = 150.41, CFI = 0.901, RMSEA = 0.088), suggesting structural equivalence. Metric invariance was supported (Δχ²(9) = 4.75, p = 0.856, CFI = 0.907, RMSEA = 0.081), and scalar invariance was also established (Δχ²(9) = 11.88, p = 0.220, CFI = 0.903, RMSEA = 0.079). These results support full measurement invariance across gender (Table 6 ). Table 6 Measurement Invariance Across Gender for the 11-item OLBI-MS (n = 195) Model χ 2 (df) Δχ²(df) p-value (Δχ²) CFI RMSEA RMSEA 90% CI SRMR Configural 150.41 (86) - - 0.901 0.088 0.064 0.111 0.084 Metric 155.16 (95) 4.75 (9) 0.856 0.907 0.081 0.057 0.103 0.086 Scalar 167.04 (104) 11.88 (9) 0.220 0.903 0.079 0.056 0.100 0.090 Δχ² and p-values represent chi-square difference tests comparing nested models. Configural invariance tests the same factor structure across groups; metric invariance constrains factor loadings to be equal; scalar invariance further constrains item intercepts. CFI = Comparative fit index, RMSEA = Root mean squared error of approximation, SRMR = Standardised root mean square residual, OLBI-MS = Oldenburg Burnout Inventory–Medical Student, CI = Confidence interval. [Table 6 near here] Internal consistency and reliability Internal consistency for the 11-item OLBI-MS was acceptable to good (alpha = 0.77–0.83; omega = 0.76–0.83). Test–retest reliability among 185 participants was strong (ICC = 0.802–0.845). Other scales demonstrated good internal consistency, except for the VQ–Obstruction subscale (alpha = 0.59), which was consistent with previous findings (Table 7 ). Table 7 Descriptive Statistics and Reliability for Study Measures Scale No. of Items Mean (SD) Cronbach’s alpha McDonald’s omega ICC (Retest, n = 185) OLBI-MS-D (11-item version) 6 2.26 (0.52) 0.83 0.83 0.822 OLBI-MS-E (11-item version) 5 2.47 (0.56) 0.77 0.76 0.831 MBI-GS-EX 5 2.85 (1.33) 0.87 - - MBI-GS-CY 5 1.68 (1.23) 0.78 - - MBI-GS-PE 6 2.65 (1.29) 0.84 - - WAAQ 7 27.08 (6.90) 0.90 - - VQ–Progress 5 18.62 (5.67) 0.81 - - VQ–Obstruct 5 15.57 (5.06) 0.59 - - PDDS 12 24.23 (5.36) 0.84 - - PHQ-9 9 4.41 (3.71) 0.79 - - Cronbach’s alpha and McDonald’s omega were calculated to assess internal consistency. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC) in a subsample of participants who completed the OLBI-MS twice (n = 185). A hyphen (–) indicates that the coefficient was not applicable or not estimated in this study. D = Disengagement, E = Exhaustion, CY = Cynicism, EX = Exhaustion, PE = Personal efficacy, SD = Standard deviation, OLBI-MS = Oldenburg Burnout Inventory–Medical Student, MBI-GS = Maslach Burnout Inventory-General Survey, WAAQ = Work-related Acceptance and Action Questionnaire, VQ = Valuing Questionnaire, PDDS = Perceived Devaluation-Discrimination Scale, PHQ-9 = Patient Health Questionnaire-9. [Table 7 near here] Hypotheses for construct validity All variables showed acceptable skewness (< 2.0) and kurtosis (< 7.0), justifying use of Pearson correlations (Table 8 ). Hypothesis testing results were as follows: Table 8 Hypothesis testing for construct validity 1 2 3 4 5 6 7 8 9 10 11 1 OLBI-MS-D (11 items) 1 2 OLBI-MS-E (11 items) 0.28 1 3 MBI-GS-CY 0.61 0.42 1 4 MBI-GS-EX 0.38 0.73 0.51 1 5 MBI-GS-PE -0.31 -0.17 -0.15 -0.07 1 6 WAAQ -0.25 -0.34 -0.28 -0.33 0.31 1 7 VQ-Progres -0.32 -0.36 -0.34 -0.35 0.53 0.50 1 8 VQ-Obsrtruct 0.30 0.29 0.33 0.28 -0.25 -0.34 -0.25 1 9 PHQ-9 0.32 0.59 0.47 0.55 -0.28 -0.35 -0.42 0.43 1 10 PDDs 0.21 0.17 0.16 0.11 -0.08 -0.18 -0.14 0.15 0.18 1 11 Mistreatment 0.37 0.13 0.25 0.20 -0.06 -0.03 -0.04 0.15 0.18 0.09 1 Bold indicates p < 0.01; italic indicates p < 0.05. Underline indicates the relationships described in the hypotheses. D = Disengagement, E = Exhaustion, CY = Cynicism, EX = Exhaustion, PE = Personal efficacy, OLBI-MS = Oldenburg Burnout Inventory–Medical Student, MBI-GS = Maslach Burnout Inventory-General Survey, WAAQ = Work-related Acceptance and Action Questionnaire, VQ = Valuing Questionnaire, PDDS = Perceived Devaluation-Discrimination Scale, PHQ-9 = Patient Health Questionnaire-9 Hypothesis 1 Disengagement correlated more strongly with Cynicism (r = 0.61) than with Exhaustion (r = 0.38). Hypothesis 2 Exhaustion correlated more strongly with MBI-GS Exhaustion (r = 0.73) than with Cynicism (r = 0.42). Hypothesis 3 Both subscales were negatively correlated with Professional Efficacy (r = − 0.31 and − 0.17). Hypothesis 4 6 : Disengagement and Exhaustion were negatively associated with WAAQ (r = − 0.25 and − 0.34), VQ–Progress (r = − 0.32 and − 0.36), and positively with VQ–Obstruction (r = 0.30 and 0.29). Hypothesis 7 PHQ-9 scores correlated positively with both subscales (r = 0.32 and 0.59). Hypothesis 8 Perceived stigma (PDDS) showed weak but positive correlations (r = 0.21 and 0.17). Hypothesis 9 Mistreatment correlated moderately with Disengagement (r = 0.37) and weakly with Exhaustion (r = 0.13), consistent with prior findings. [Table 8 near here] Discussion The Japanese version of the 11-item OLBI-MS version demonstrated satisfactory psychometric properties, including internal consistency, test–retest reliability, structural validity, and evidence for construct validity based on hypothesis testing. These findings support its use as a brief and robust tool for assessing burnout in medical students during clinical clerkships. Sociodemographic characteristics This study achieved a high response rate from fifth- and sixth-year medical students at two universities. The participants’ demographic characteristics, such as alcohol consumption, extracurricular activities, and cohabitation status, were similar to those reported in previous national studies [ 8 ]. The prevalence rates of exhaustion, cynicism, low professional efficacy, and severe burnout were also consistent with earlier findings. Structural validity The results supported a two-factor structure for the revised 11-item OLBI-MS, with all items demonstrating factor loadings of 0.55 or above. In contrast, the two-factor model of the original 16-item version was not supported. This aligns with findings from a previous study involving second-year medical students in the United States, which similarly failed to validate the two-factor structure of the 16-item scale [ 58 ]. A 10-item two-factor model, nearly identical to the present 11-item version except for the exclusion of item D6 (sickened), was proposed in that study. However, the current 11-item version exhibited superior model fit, with stronger item loadings, particularly for D2 (talk) and D3 (think), which had loadings below 0.5 in the 10-item model. This improvement may reflect refinements in item wording during translation, enhancing coherence and clarity. Additionally, the present sample comprised clinical-year students with greater clinical exposure than the preclinical second-year cohort used in the earlier study. The 16-item three-factor model in the current study demonstrated satisfactory fit, yet item E1 (manage) loaded significantly on a third factor not clearly aligned with self-efficacy, a burnout dimension defined by the World Health Organization [ 1 ]. This pattern was also observed in Runyon et al. (2022). Furthermore, items D5 (disconnected), D7 (imagine), and E3 (pressure) exhibited weak loadings across both the two- and three-factor solutions. By contrast, E6 (energised) consistently loaded on disengagement rather than exhaustion, a finding consistent with previous validation work on the English-language OLBI [ 14 ]. Taken together, these findings support the revised 11-item version as a more structurally valid representation of the OLBI’s intended two-factor model of burnout—disengagement and exhaustion—compared to the original 16-item form. Bifactor model and method effects The bifactor model revealed good fit indices and supported the presence of a general burnout factor alongside domain-specific (Disengagement and Exhaustion) and method-related (item wording) factors. This model outperformed the standard two-factor model and highlighted the impact of reverse-worded items on measurement variance. These findings imply that burnout symptoms among medical students may be understood as comprising both a general latent tendency (e.g., overall burnout severity) and two domain-specific expressions (disengagement and exhaustion), with reverse-worded items contributing additional methodological variance. This highlights the importance of accounting for method effects when interpreting multidimensional burnout scales such as the OLBI. The results are consistent with previous research indicating that bifactor models can improve construct validity by isolating artifactual variance due to item wording [ 52 , 53 ]. Incorporating a method factor helped clarify the substantive structure of the OLBI-MS and supports its psychometric robustness in the Japanese context. Measurement invariance across gender This study demonstrated that the Japanese version of the 11-item OLBI-MS exhibited full measurement invariance across gender groups. Specifically, configural, metric, and scalar invariance were supported, indicating that the factor structure, factor loadings, and item intercepts were equivalent for male and female medical students. These findings suggest that the OLBI-MS assesses the constructs of disengagement and exhaustion consistently regardless of gender, allowing for meaningful comparisons of latent burnout levels between male and female groups. Invariance testing is critical for ensuring that any observed differences in burnout scores between groups reflect true differences in the underlying construct rather than measurement artifacts. The establishment of scalar invariance is particularly noteworthy, as it justifies the use of observed mean scores to compare burnout levels across gender without bias. Previous studies have raised concerns about gender-related response styles in burnout measurement tools, especially when reverse-coded items are involved [ 59 ]. However, the use of bifactor modeling and subsequent invariance testing in this study provides reassurance that the Japanese OLBI-MS functions similarly across genders even in the presence of mixed item wordings. Furthermore, the ability to validly compare burnout symptoms between male and female medical students is especially important in Japan, where gender-related differences in stress, expectations, and mistreatment during clinical training have been documented [ 13 ]. The present results support the OLBI-MS as a gender-equitable instrument in this context. Internal consistency and reliability Both subscales of the OLBI-MS demonstrated acceptable to good internal consistency (alpha and omega ≥ 0.77) and strong test–retest reliability (ICC = 0.80–0.85). These results meet standard psychometric criteria and align with previous validation studies in other languages [ 14 , 26 ]. The results also highlight the advantages of using model-based reliability indices such as McDonald's omega in conjunction with Cronbach’s alpha. While alpha assumes equal factor loadings and uncorrelated errors, omega provides a more realistic estimate of reliability under a latent variable framework, especially for multidimensional instruments like the OLBI-MS [ 44 ]. However, the VQ–Obstruction subscale showed lower internal consistency (alpha = 0.59), a finding consistent with past Japanese research [ 8 , 11 , 60 , 61 ]. Interpretations involving this measure should therefore be made with caution. Hypothesis testing for construct validity The predefined hypotheses were generally supported, providing robust evidence for the construct validity of the Japanese OLBI-MS. Consistent with expectations, the Disengagement subscale showed a strong positive correlation with Cynicism from the MBI-GS (r = 0.61), while the Exhaustion subscale correlated strongly with MBI-GS Exhaustion (r = 0.73), confirming Hypotheses 1 and 2. Both subscales were also inversely associated with Professional Efficacy (r = − 0.31 and − 0.17), supporting Hypothesis 3 and aligning with prior work [ 14 ]. Regarding psychological flexibility, both subscales were negatively correlated with WAAQ scores (r = − 0.25 and − 0.34), consistent with Hypothesis 4 . In line with Hypotheses 5 and 6, Disengagement and Exhaustion were negatively associated with value progress (VQ–P) and positively with value obstruction (VQ–O). These findings suggest that decreased value-oriented action and lower psychological flexibility are closely linked to burnout, as demonstrated in previous studies with medical students [ 8 , 10 ]. Supporting Hypothesis 7 , depressive symptoms measured via PHQ-9 were moderately correlated with Disengagement (r = 0.32) and strongly with Exhaustion (r = 0.59), corroborating earlier evidence that positions emotional exhaustion as a shared core of burnout and depression [ 27 , 62 – 65 ]. Disengagement may serve to delineate overlapping yet distinct symptomatology [ 66 ]. Hypothesis 8 was modestly supported, with weak positive correlations observed between both OLBI-MS subscales and perceived mental health stigma (r = 0.21 and 0.17), consistent with reports linking burnout with elevated stigma perceptions [ 12 , 67 ]. Hypothesis 9 received partial support. Mistreatment experiences showed a moderate correlation with Disengagement (r = 0.37) but only a weak association with Exhaustion (r = 0.13), in line with prior findings highlighting interpersonal mistreatment as a driver of attitudinal withdrawal rather than emotional depletion[ 13 , 42 , 68 ]. Overall, the results affirm the construct validity of the Japanese OLBI-MS by demonstrating meaningful associations across affective, behavioural, and interpersonal domains relevant to burnout. Strengths, limitations, and implications This study presents several notable strengths. It followed a rigorous methodological framework in line with the latest COSMIN guidelines [ 23 ], enabling a comprehensive assessment of the scale’s psychometric properties. The sample was drawn from two geographically and institutionally diverse university hospitals that implement clinical clerkship models reflective of Japan’s evolving medical education system. By targeting fifth- and sixth-year students actively involved in clinical care, the study captured the relevant contextual stressors and psychological demands associated with burnout in authentic settings. Nonetheless, the study has limitations. First, the scale’s responsiveness—its ability to detect changes over time—was not assessed. Second, although the translation process adhered to COSMIN standards, including expert review and back-translation, formal testing of cross-cultural validity was not conducted. Third, while measurement invariance by gender was established, the findings may not generalise to preclinical students or those outside clinical training contexts. Importantly, the study coincided with a transformative period in Japan’s medical education, marked by a national shift from passive observation to active team-based clinical clerkships, prompted by the ECFMG’s 2023 Accreditation policy [ 69 ]. This reform aligns Japanese training with international benchmarks and underscores the need for culturally validated, globally relevant burnout measures. The Japanese OLBI-MS, as validated here, addresses this need by offering a concise, psychometrically robust instrument suitable for use in clinical education settings. It provides a practical foundation for cross-national research and targeted intervention development within Japan’s changing medical training landscape. Conclusions The Japanese version of the OLBI-MS demonstrated sound psychometric properties, including structural validity, internal consistency, test–retest reliability, measurement invariance across gender, and construct validity supported by hypothesis testing. The shortened 11-item version, characterised by a clearly defined two-factor structure (Disengagement and Exhaustion), exhibited superior model fit and interpretability compared to the original 16-item version. Bifactor modelling further supported the presence of both substantive and method-related variance, highlighting the relevance of accounting for reverse-worded item effects in burnout measurement. Additionally, the study established significant associations between burnout and related psychological constructs, including depression, psychological flexibility, stigma, and mistreatment experiences. These findings support the Japanese OLBI-MS as a theoretically grounded, culturally adapted, and psychometrically robust instrument for assessing burnout among medical students participating in clinical clerkships. The 11-item version offers a practical and efficient tool for screening and tracking burnout in medical education. Further research is recommended to evaluate its generalisability to other educational levels, health professions, and cultural contexts. Future studies should also examine its longitudinal validity, particularly its sensitivity to change, to inform its use in intervention research and well-being promotion. Abbreviations AAQ-2 Acceptance and Action Questionnaire–II CFI Comparative Fit Index CFA Confirmatory Factor Analysis COR Conservation of Resources (theory) CY Cynicism (subscale of MBI-GS) df Degrees of freedom ECFMG Educational Commission for Foreign Medical Graduates EFA Exploratory Factor Analysis EX Exhaustion (subscale of MBI-GS) ICC Intraclass Correlation Coefficient JD–R Job Demands–Resources (model) KMO Kaiser–Meyer–Olkin (measure of sampling adequacy) MAP Minimum Average Partial (test) MBI-GS Maslach Burnout Inventory–General Survey OLBI-MS Oldenburg Burnout Inventory–Medical Student PDDS Perceived Devaluation–Discrimination Scale PE Professional Efficacy (subscale of MBI-GS) PHQ-9 Patient Health Questionnaire-9 RMSEA Root Mean Square Error of Approximation SD Standard Deviation SRMR Standardized Root Mean Square Residual TLI Tucker–Lewis Index UMIN University Hospital Medical Information Network (Clinical Trials Registry) VQ Valuing Questionnaire VQ-O Valuing Questionnaire – Obstruction (subscale) VQ-P Valuing Questionnaire – Progress (subscale) WAAQ Work-related Acceptance and Action Questionnaire WHO World Health Organization Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Nagoya City University Graduate School of Medical Sciences (Approval No.: 60-22-0087) and registered with the University Hospital Medical Information (UMIN) Clinical Trials Registry (UMIN000048572), registered on 4 August 2022, prior to commencement. The study was conducted in accordance with the Declaration of Helsinki. All participants received written and verbal information about the study and provided informed consent electronically by checking the consent box before participation. All participants received written and verbal information about the study before beginning their psychiatric clinical clerkship. They were assured that not participating would not impact their grades and that their privacy would be protected. Participants were given time to consider participation. Those who agreed accessed the Google Form online and checked the consent box. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Supplementary materials are provided as Additional files 1–4. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding TW was supported in this research by Grant-in-Aid for Scientific Research (JP22K10420) from the Japan Society for the Promotion of Science (https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-22K10420/). The funders had no involvement in the design of this study, the collection, analysis, and interpretation of the data, or in the writing of the manuscript. Author contributions TW, TN, NS, and MK conceptualised and designed the study. The data were acquired by TW, IO, MK, and OT. The analysis and interpretation of the data was conducted by TW, MS, and AT. TW wrote the manuscript. OT and AK made significant contributions to the revision of the manuscript. All authors read and approved the final version of the manuscript. 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Psychiatry Res. 2024;335:115828. Yavuz KF, Nalbant A, Ulusoy S, Esen B, Burhan HS, Kara T. Burned out and avoided: stigmatizing processes among psychiatrists. Psychiatr Danub. 2020;32(Suppl 4):463–70. Sudol NT, Guaderrama NM, Honsberger P, Weiss J, Li Q, Whitcomb EL. Prevalence and nature of sexist and racial/ethnic microaggressions against surgeons and anesthesiologists. JAMA Surg. 2021;156(5):e210265. Educational Commission for Foreign Medical Graduates. Recognized accreditation policy. [Internet]. Available from: https://www.ecfmg.org/accreditation/ . Accessed 23 Sep 2025. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1FigureS1parallelanalysis16items.pdf Additional file 1: Figure S1. Parallel analysis scree plot for the 16-item OLBI-MS (Model 1). Additionalfile2FigureS2parallelanalysis11items.pdf Additional file 2: Figure S2. Parallel analysis scree plot for the 11-item OLBI-MS (Model 2). Additionalfile3TableS1JapaneseOLBIMS16itemsEnglishtranslation.docx Additional file 3: Table S1. Japanese OLBI-MS (16 items)—English translation of item texts and response options. Additionalfile4TableS2JapaneseOLBIMS11itemsEnglishtranslation.docx Additional file 4: Table S2. Japanese OLBI-MS (11 items)—English translation of item texts and response options. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 14 Nov, 2025 Editor assigned by journal 05 Nov, 2025 Editor invited by journal 20 Oct, 2025 Submission checks completed at journal 18 Oct, 2025 First submitted to journal 18 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7717852","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532680570,"identity":"39b6f2b5-2c6f-4616-9a9c-cd62a540bc6f","order_by":0,"name":"Takafumi Watanabe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYFAC5oMPPjAwMDagCbPh1MDDwJZsOINELTxm0jxYtOAG9hIJxsa2OTayDdLNj198bLNj4J+RwPjhBwNfHk5bJBISH+duSzNukDlmZjmzLZlB4kYCs2QPA1sxTi3SCYeNc7cdTmyQSDAz5m1jrt8gkcAgDfRLIi6n8kgntklbbvsP1JL+DailnsFAIoH5N34tyWzSjNsOALXkGD/mbTsM0sKG35b7z5gNe7clG7dJ5JQxzjh3nEHizMM2yx4D3H5h7zn/8cHPbXay/RLpmz98KKtm4G9PPnzjR8UxnCEGB8CoY5NgBEcgKI4MjiUQ1AIEzB8Y/sA5NURpGQWjYBSMghEBAAe1UafyVTm6AAAAAElFTkSuQmCC","orcid":"","institution":"Nagoya City University","correspondingAuthor":true,"prefix":"","firstName":"Takafumi","middleName":"","lastName":"Watanabe","suffix":""},{"id":532680571,"identity":"f3881ee3-b7eb-4cf7-91bc-916cbb88bcb2","order_by":1,"name":"Ichiro M. Omori","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Ichiro","middleName":"M.","lastName":"Omori","suffix":""},{"id":532680572,"identity":"3559fe43-1a5c-400a-8add-fbfff1350abd","order_by":2,"name":"Tomohiro Nakaguchi","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Tomohiro","middleName":"","lastName":"Nakaguchi","suffix":""},{"id":532680573,"identity":"2b405c44-2557-4740-aa7e-394cdf493b05","order_by":3,"name":"Nao Shiraisi","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Nao","middleName":"","lastName":"Shiraisi","suffix":""},{"id":532680574,"identity":"63a9baf9-e236-4b26-8cee-c7f1e89736cf","order_by":4,"name":"Mie Sakai","email":"","orcid":"","institution":"Nagoya City University","correspondingAuthor":false,"prefix":"","firstName":"Mie","middleName":"","lastName":"Sakai","suffix":""},{"id":532680575,"identity":"1542b11d-e0a1-4fa9-8c20-55a067b267c8","order_by":5,"name":"Masaki Kondo","email":"","orcid":"","institution":"National Center of Neurology and Psychiatry","correspondingAuthor":false,"prefix":"","firstName":"Masaki","middleName":"","lastName":"Kondo","suffix":""},{"id":532680576,"identity":"eeb592a1-2bc2-4970-80e6-d1a383ef689c","order_by":6,"name":"Masako Kakizaki","email":"","orcid":"","institution":"Nagoya City University Graduate School of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Masako","middleName":"","lastName":"Kakizaki","suffix":""},{"id":532680577,"identity":"58299501-d985-470a-bb72-262530311391","order_by":7,"name":"Osamu Takakuwa","email":"","orcid":"","institution":"Nagoya City University Graduate School of Medical 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06:16:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19926,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Figure S1. Parallel analysis scree plot for the 16-item OLBI-MS (Model 1).\u003c/p\u003e","description":"","filename":"Additionalfile1FigureS1parallelanalysis16items.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7717852/v1/d267eaf5e81d4c61ed5e7085.pdf"},{"id":94059426,"identity":"0d2c8cc3-0368-447d-a609-60c8eeda2770","added_by":"auto","created_at":"2025-10-22 06:08:27","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19941,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Figure S2. Parallel analysis scree plot for the 11-item OLBI-MS (Model 2).\u003c/p\u003e","description":"","filename":"Additionalfile2FigureS2parallelanalysis11items.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7717852/v1/eac2db3de61018d33f8052df.pdf"},{"id":94058999,"identity":"7244057f-802a-458a-ba8d-37cdce242177","added_by":"auto","created_at":"2025-10-22 06:00:27","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27510,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Table S1. Japanese OLBI-MS (16 items)—English translation of item texts and response options.\u003c/p\u003e","description":"","filename":"Additionalfile3TableS1JapaneseOLBIMS16itemsEnglishtranslation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7717852/v1/d86d08dadc799fc990146d45.docx"},{"id":94059427,"identity":"9d01477b-5488-4d2c-a4bb-a28535d51330","added_by":"auto","created_at":"2025-10-22 06:08:27","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26594,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: Table S2. Japanese OLBI-MS (11 items)—English translation of item texts and response options.\u003c/p\u003e","description":"","filename":"Additionalfile4TableS2JapaneseOLBIMS11itemsEnglishtranslation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7717852/v1/1b7b4aaed20bf9c2211b3601.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eValidity and Reliability of a Japanese Version of the Oldenburg Burnout Inventory–Medical Student: A Study on Students in Clinical Clerkships\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eBurnout is a work-related condition defined by energy depletion, mental detachment from one's work, negative or cynical attitudes, and reduced professional efficacy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Originally described by Freudenberger in the 1970s, it has since been conceptualised through various psychological frameworks and evaluated using psychometric tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Maslach and colleagues outlined three core dimensions—exhaustion, depersonalisation (later termed cynicism), and diminished personal accomplishment (later termed professional efficacy)—based on empirical studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSeveral theoretical models have attempted to explain how burnout develops. Early frameworks proposed a sequential model: high demands lead to exhaustion, which in turn results in cynicism and reduced professional efficacy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. More recent theories, including the Conservation of Resources (COR) theory and the Job Demands–Resources (JD–R) model, focus on the imbalance between demands and available resources as central to burnout [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBurnout prevalence rises sharply during the clinical year of medical school [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent study found that 28.8% of medical students reported high exhaustion, 14.6% high cynicism, 87.6% low professional efficacy, and 34.1% severe overall burnout [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Associated risk factors include symptoms of depression and anxiety [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], lower psychological flexibility—defined as the ability to pursue valued goals without avoiding uncomfortable internal experiences [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], —mental health stigma [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and experiences of mistreatment during clinical training [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Maslach Burnout Inventory–General Survey (MBI-GS) is widely used but has known limitations. Its Professional Efficacy (PE) subscale is positively worded, in contrast to the negatively worded Exhaustion (EX) and Cynicism (CY) subscales, increasing the risk of artificial factor structures. Furthermore, the PE subscale often shows inconsistent associations with other burnout domains and related outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Oldenburg Burnout Inventory (OLBI), developed using the JD–R model, addresses these limitations by assessing only two dimensions—Exhaustion and Disengagement—with both positively and negatively worded items [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A student-specific version (OLBI-MS) is used in major U.S. surveys [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and numerous studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, no validated Japanese version of the OLBI-MS exists. While a version has been validated for nurses [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], medical students face qualitatively distinct challenges as learners and novice clinicians, including role transition, identity formation, and ethical stressors. Japan’s medical education system is shifting from passive observation to team-based clinical clerkships [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], heightening the need for context-specific assessment tools. Our pilot study of an ACT-based intervention also highlighted the need for brief, reliable, and culturally validated burnout measures [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eObjectives and Hypotheses\u003c/h3\u003e\n\u003cp\u003eThis study aimed to develop a Japanese version of the OLBI-MS and evaluate its psychometric properties among medical students in clinical clerkships. We hypothesised that the original two-factor structure (Disengagement and Exhaustion) would be replicated and that the instrument would demonstrate acceptable reliability and construct validity. Specifically:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDisengagement would correlate positively with cynicism (CY) from the MBI-GS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eExhaustion would correlate positively with exhaustion (EX) from the MBI-GS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBoth factors would correlate negatively with professional efficacy (PE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypotheses 4–6\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eIn terms of psychological flexibility, both factors would (4) be negatively associated with the ability to engage in work-related activities without avoiding unpleasant internal experiences, (5) negatively with perceived progress toward personally important values, and (6) positively associated with perceived barriers to value-consistent behaviour.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 7\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBoth factors would be positively associated with depressive symptoms.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 8\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBoth factors would be positively associated with perceived mental health stigma.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 9\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBoth factors would be positively associated with experiences of mistreatment in clinical contexts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e\n\n\n\n\n\n\n\n\n\n"},{"header":"Methods","content":"\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eThis multicentre, cross-sectional study was conducted in accordance with classical test theory and the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) checklist for evaluating the measurement properties of patient-reported outcome measures [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Based on COSMIN guidelines, a minimum of 100 participants is required for testing construct validity, internal consistency, and reliability. Additionally, a heuristic recommendation of 7 participants per item suggests 112 cases for the 16-item OLBI-MS. Anticipating potential invalid responses, we set the target sample size at 160 by multiplying the number of items by 10.\u003c/p\u003e\u003cp\u003eAn a priori power analysis was conducted using G*Power 3.1 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] to estimate the required sample size for confirmatory factor analysis (CFA). Assuming a two-factor model with 16 observed variables (approx. 100 degrees of freedom), a medium effect size (Cohen’s w = 0.2), alpha = 0.05, and power = 0.80, the minimum sample size was estimated at 240. While our final sample size of 195 fell slightly below this threshold, it remained within the acceptable range for structural equation modelling under realistic constraints [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003eSetting and participants\u003c/h3\u003e\u003cp\u003e The participants were 87 fifth- and 119 sixth-year medical students undergoing clinical clerkship in psychiatry at Nagoya City University Hospital from 19 December 2022 to 24 July 2023 and at the University of Fukui Hospital from 10 January 2023 to 22 December 2023 in Japan (where the school year begins in April). Both hospitals are affiliated with medium-sized university medical schools and train Japanese physicians. These hospitals were selected for their clinical clerkship programs, which allow students to participate in clinical training as part of a team.\u003c/p\u003e\u003cp\u003e This study was approved by the Institutional Review Board of Nagoya City University Graduate School of Medical Sciences and registered with the University Hospital Medical Information (UMIN) Clinical Trials Registry (Contact No. UMIN000048572), registered on 4 August 2022, before starting. Prior to the commencement of their psychiatric clinical clerkship, all participants received written and verbal information about the study. They were assured that non-participation would not affect their grades and that their privacy would be protected. Participants were given ample time to decide whether to participate. Those who agreed accessed the Google Form online and checked the consent box in the instructions. This procedure constituted informed consent, and the study adhered to the Declaration of Helsinki.\u003c/p\u003e\u003ch3\u003eData collection\u003c/h3\u003e\u003cp\u003eThe data were collected anonymously through an online survey using Google Forms. Respondents were emailed within 7 to 14 days of completing the first survey to respond again to the OLBI-MS for retest reliability validation. Participants who returned both surveys were incentivised with a 500-yen email gift certificate.\u003c/p\u003e\u003ch3\u003eInstruments\u003c/h3\u003e\u003cp\u003eThe instruments used in the study included the OLBI-MS, MBI-GS, Work-related Acceptance and Action Questionnaire (WAAQ), Valuing Questionnaire (VQ), Perceived Devaluation-Discrimination Scale (PDDS), and Patient Health Questionnaire-9 (PHQ-9). Data were collected on the frequency of mistreatment by faculty and residents, as well as various demographic characteristics such as sex, age, alcohol consumption (more than three per week), participation in extracurricular activities, presence of housemates, having a part-time job, history of enrolment in other faculties, presence of a spouse, and parenting status.\u003c/p\u003e\u003ch2\u003eOldenburg Burnout Inventory-Medical Student (OLBI-MS)\u003c/h2\u003e\u003cp\u003eThe OLBI-MS is unique because it consists of disengagement and exhaustion, including mental and physical aspects, with both positive and negative wording for each factor’s items in psychometric measures. It uses a 4-point Likert scale ranging from 1 (strongly agree) to 4 (strongly disagree), with eight items for each factor. If needed, items are reverse scored, and an average score is calculated for each factor. Higher scores indicate a higher sense of disengagement and exhaustion [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Disengagement is described as distancing or negative attitude toward work. Exhaustion refers to cognitive, affective, and physical fatigue resulting from work demands. The average total scores for the disengagement and exhaustion subscales are also provided [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003ch3\u003eTranslation of the OLBI-MS into Japanese\u003c/h3\u003e\u003cp\u003e The scale was translated into Japanese following the COSMIN guidelines. Two native Japanese psychiatrists, both with sufficient knowledge of English and one of them with knowledge and experience in burnout in medical professionals and medical students independently translated the original OLBI-MS into Japanese. One of them had experience in burnout among medical professionals and students. Permission was obtained from Dr. Evangelia Demerouti, the original developer of the OLBI-MS. A committee of three native Japanese psychiatrists, with English proficiency and experience in burnout research, reviewed the translations and created a preliminary Japanese version. This version was then back-translated into English by a psychiatrist and a language expert unfamiliar with the original OLBI-MS. The translations were reviewed, merged, and revised by the committee. Dr. Demerouti confirmed the content similarity with the original version. Eight medical students in clinical training received the preliminary Japanese version of the OLBI-MS to check for inappropriate expressions, suggesting alternatives that were incorporated to finalise the Japanese version of the OLBI-MS.\u003c/p\u003e\u003ch3\u003eMaslach Burnout Inventory-General Survey (MBI-GS)\u003c/h3\u003e\u003cp\u003eThe MBI includes the MBI-GS (student version) for university students. Medical students work as interpersonal helpers during clinical rotations. As the OLBI-MS has shown structural validity with the MBI-GS, it was used. The scale has 16 self-administered items [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] rated on a 7-point Likert scale from 0 (not at all) to 6 (every day). It includes three factors: EX, CY, and PE, with average scores for each factor calculated. The reliability and validity of the Japanese version have been confirmed [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. High exhaustion is defined as EX ≥ 3.2, high cynicism as CY ≥ 2.2, and low job efficacy as PE ≤ 4.0 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Showing one or more symptoms of burnout in addition to fatigue is a more appropriate indicator of severe burnout in the general population. This study also adopted the additional criterion of ‘exhaution + 1’ [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eWork-related Acceptance and Action Questionnaire (WAAQ)\u003c/h2\u003e\u003cp\u003eThe WAAQ assesses psychological flexibility in the work environment. Each item is rated on a scale of 1–7, with a total possible score of 7–49. Higher scores indicate better ability to manage unpleasant thoughts, feelings, impulses, and sensations at work without avoidance. Both the original and Japanese versions of the WAAQ have been proven to be reliable and valid [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].. Avoiding unpleasant internal experiences has been linked to burnout [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eValuing Questionnaire (VQ)\u003c/h2\u003e\u003cp\u003eThe VQ consists of 10 items related to value-directed behaviours. Two subscales measure progress (VQ-P) and obstruction (VQ-O). Each factor includes five items rated on a scale of 0 to 6, totalling a score of 0 to 30. VQ-O is correlated with the Acceptance and Action Questionnaire-2 (AAQ-2), which assesses psychological inflexibility. The reliability and validity of the original version [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and the Japanese version [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] have been documented. Decreased value-directed behaviours have been linked to burnout [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003ePatient Health Questionnaire-9 (PHQ-9)\u003c/h2\u003e\u003cp\u003eThe PHQ is a self-administered questionnaire used by primary care physicians to diagnose and assess eight types of mental disorders [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The PHQ-9 is a subset of the questionnaire focused on major depressive disorder. It is scored on a 9-item, 4-point Likert scale, with higher scores reflecting most severe depressive symptoms. The original version, as well as the Japanese version, have been shown to be reliable and valid [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Depression and burnout are shown to be positively correlated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003ePerceived Devaluation-Discrimination Scale (PDDS)\u003c/h2\u003e\u003cp\u003eThe PDDS is a 12-item scale that assesses stigma toward individuals who have undergone psychiatric treatment. It is aimed at the general public, patients, and their families. The descriptive wording of the items helps prevent bias in responses. Previous research has shown a correlation between burnout and stigma toward mental illness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], with higher burnout scores predicting increased stigma. The original PDDS version rates each item on a 6-point scale, giving a total score between 6 and 72. A higher total score suggests greater perceived stigma [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The Japanese version uses a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with 4 points for each item. The total score ranges from 4 to 48, and its reliability and validity have been verified [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eFrequency of mistreatment by faculty or residents\u003c/h2\u003e\u003cp\u003eThe question used by Cook et al. to assess mistreatment frequency in medical school was: ‘Have you ever been mistreated by a faculty member or resident in medical school?’ Responses were collected using a 4-point Likert scale with options including: ‘never’, ‘once or twice’, ‘several times’, and ‘many times’ [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This is relevant due to reported associations between mistreatment and burnout in medical training [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted in accordance with the COSMIN checklist [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], using a two-tailed significance level of 0.05. CFA was performed using Mplus version 8.8, while all other analyses were conducted using R version 4.4.1, employing the relevant packages: lavaan, semTools, psych, irr, and GPArotation. Prior to analysis, reverse-coded items were rescored to ensure consistency in item direction.\u003c/p\u003e\u003cp\u003eThe OLBI-MS was developed using a reflective model. After collecting data, several statistical analyses were carried out to confirm the validity and reliability of the OLBI-MS. Internal consistency was assessed using Cronbach’s alpha and McDonald’s omega, calculated with the semTools and psych packages in R [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Test–retest reliability was evaluated using intraclass correlation coefficients (ICC) based on a two-way random effects model with absolute agreement, implemented using the irr package. An ICC of ≥ 0.70 was considered acceptable [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCFA was utilised using Mplus to evaluate structural validity, assuming a 2-factor model corresponding to the disengagement and exhaustion factors. The model fit indices used were standardised root mean square residuals (SRMR), comparative fit index (CFI), and root mean square error of approximation (RMSEA). A good fit was indicated by χ2/df ≤ 5 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], CFI ≥ 0.95 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], SRMR ≤ 0.08, and RMSEA ≤ 0.06 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIf the CFA model showed poor fit, exploratory factor analysis (EFA) was conducted using the psych and GPArotation packages. Sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s test of sphericity. The number of factors was determined using the eigenvalue \u0026gt; 1 criterion, scree plot, parallel analysis [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], and minimum average partial (MAP) test [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Maximum likelihood extraction with oblimin rotation was applied. Items with low factor loadings (\u0026lt; 0.50) or low inter-item correlations (\u0026lt; 0.60) were considered for removal during refinement.\u003c/p\u003e\u003cp\u003eGiven that the OLBI-MS includes both positively and negatively worded items, a bifactor model was estimated using lavaan to assess method effects. The model specified one general burnout factor, two group-specific factors (Disengagement and Exhaustion), and one method factor representing variance due to item wording. All latent factors were modelled as orthogonal. Fit indices were compared to the standard two-factor model [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMeasurement invariance across gender (male vs. female) was tested using multiple-group CFA in lavaan. Invariance was assessed hierarchically via three nested models: (1) configural (unconstrained), (2) metric (constraining factor loadings), and (3) scalar (constraining loadings and intercepts). Model fit was evaluated using CFI, RMSEA, and SRMR. Invariance was judged using chi-square difference tests and accepted cut-off values: ΔCFI ≤ 0.01 and ΔRMSEA ≤ 0.015 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo assess construct validity, Pearson correlation coefficients were calculated between the OLBI-MS and theoretically related constructs (MBI-GS, WAAQ, VQ, PHQ-9, PDDS, and mistreatment frequency). Correlation strength was interpreted using thresholds of ± 0.50 (strong), ± 0.30 (moderate), and ± 0.10 (weak) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. These analyses were used to test nine predefined hypotheses (Hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e – Hypothesis \u003cspan refid=\"FPar7\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e\u003cp\u003eOf the 206 medical students invited to participate, 195 completed the survey (response rate\u0026thinsp;=\u0026thinsp;94.7%). The sample comprised 136 males (69.7%) and 59 females (30.3%), with a mean age of 24.45 years (SD\u0026thinsp;=\u0026thinsp;3.00). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the demographic characteristics and descriptive statistics for each instrument.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical Condition and Demographic Characteristics of the Participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;195)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (women)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (30.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (men)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (69.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh exhaustion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (41.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh cynicism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (35.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow professional efficacy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e174 (89.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere burnout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (37.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol consumption more than thrice per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (10.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParticipation in extracurricular activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120 (61.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence of housemates\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54 (27.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParenting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of enrolment in other faculties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (10.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaving a part-time job\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (69.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity 1 (Nagoya City)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82 (42.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity 2 (Fukui)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (57.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.45 (3.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequency of mistreatment by faculty or residents\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.82 (0.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eCut-off scores for each factor of burnout were as follows: MBI-GS-CY\u0026thinsp;\u0026ge;\u0026thinsp;2.2, MBI-GS-EX\u0026thinsp;\u0026ge;\u0026thinsp;3.2, and MBI-GS-PE\u0026thinsp;\u0026le;\u0026thinsp;4.0, indicating high cynicism, high exhaustion, and low professional efficacy, respectively. Severe burnout was defined as MBI-GS-EX\u0026thinsp;\u0026ge;\u0026thinsp;3.2 and either MBI-GS-CY\u0026thinsp;\u0026ge;\u0026thinsp;2.2 or MBI-GS-PE\u0026thinsp;\u0026le;\u0026thinsp;4.0. SD\u0026thinsp;=\u0026thinsp;Standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e near here]\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003eStructural validity\u003c/h2\u003e\u003cp\u003ewas conducted after reverse scoring to examine whether the Japanese version of the OLBI-MS preserved the original two-factor structure (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The model fit indices\u0026mdash;SRMR\u0026thinsp;=\u0026thinsp;0.114, CFI\u0026thinsp;=\u0026thinsp;0.717, and RMSEA\u0026thinsp;=\u0026thinsp;0.115\u0026mdash;indicated poor fit. Consequently, two models were specified: Model 1 included all 16 items, whereas Model 2 retained 11 items (D1, D2, D3, D4, D6, D8, E2, E4, E5, E7, and E8), selected based on factor loadings (\u0026ge;\u0026thinsp;0.5) and inter-item correlations (\u0026ge;\u0026thinsp;0.6).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConfirmatory Factor Analysis Results with All OLBI-MS Items (16 items)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItem\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubscale: Name in manuscript\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor \u003c/p\u003e\u003cp\u003eloading\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eItem-total\u003c/p\u003e\u003cp\u003ecorrelation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR square\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eI always find new and interesting aspects in my medical school work.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD1: Interest\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.559\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.613\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.790 (0.567)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.313\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIt happens more and more often that I talk about my medical school work in a negative way.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD2: Talk\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.757\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.736\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.410 (0.764)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.573\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLately, I tend to think less at medical school and do my medical school work almost mechanically.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD3: Think\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.626\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.661\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.533 (0.768)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.392\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eI find my medical school work to be a positive challenge.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD4: Challenge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.671\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.717\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e1.872 (0.680)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.450\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOver time, one can become disconnected from medical school work.\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD5: Disconnected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.513 (0.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSometimes I feel sickened by my medical school work.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD6: Sickened\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.767\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.765\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.605 (0.782)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.588\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe study of medicine is the only thing that I can imagine myself doing.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD7: Image\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.451 (0.975)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eI feel more and more engaged in my medical school work.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eD8: Engaged\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.609\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.674\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.359 (0.692)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.371\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUsually, I can manage the amount of my medical school work well.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE1: Manage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.303 (0.678)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter a day of medical school work, I tend to need more time than in the past in order to relax and feel better.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE2: Relax\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.556\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.615\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.205 (0.824)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.309\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI can tolerate the pressure of my medical school work very well.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE3: Pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.990 (0.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDuring my medical school work, I often feel emotionally drained.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE4: Drained\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.724\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.700\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.713 (0.799)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.525\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter a day of medical school, I have enough energy for leisure activities.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE5: Leisure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.441\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.625\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.174 (0.787)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.195\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhen I am at medical school, I usually feel energized.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE6: Energised\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.410 (0.708)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter a day of medical school, 1 feel worn out and weary.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE7: Weary\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.595\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.658\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.590 (0.729)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.354\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThere are days when I feel tired before I arrive at medical school.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eE8: Tired\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.730\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.674\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e2.687 (0.725)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.533\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eItalic font indicates reversed items. Bold font indicates items adopted in the revised model.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eD\u0026thinsp;=\u0026thinsp;Disengagement, E\u0026thinsp;=\u0026thinsp;Exhaustion, SD\u0026thinsp;=\u0026thinsp;Standard deviation, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e near here]\u003c/h2\u003e\u003cp\u003eEFA was subsequently performed. The data met suitability criteria with a KMO value of 0.84 and significant results on Bartlett\u0026rsquo;s test for both Model 1 (χ\u0026sup2; = 11,019.039, df\u0026thinsp;=\u0026thinsp;120, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Model 2 (χ\u0026sup2; = 683.857, df\u0026thinsp;=\u0026thinsp;55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Based on the Kaiser criterion, parallel analysis, and minimum average partial correlation, a two- or three-factor solution was plausible for Model 1, and a two-factor solution for Model 2 (Supplementary Figs.\u0026nbsp;1 and 2; see Additional files 1 and 2).. Maximum likelihood extraction with oblimin rotation was used in both models (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of the Exploratory Factor Analyses of the Original (16 Items) and Revised Measures (11 Items)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003eModel 1 \u003c/p\u003e\u003cp\u003e(16 items)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel 2 \u003c/p\u003e\u003cp\u003e(11 items)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2-factor solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003e3-factor solution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e2-factor solution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD1 (Interest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.600\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.596\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.614\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD2 (Talk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.671\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.680\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.727\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD3 (Think)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.602\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.596\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.648\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD4 (Challenge)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.756\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.739\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.746\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD5 (Disconnected)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD6 (Sickened)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.624\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.632\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.678\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD7 (Imagine)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD8 (Engaged)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.697\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.695\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.598\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE1 (Manage)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.577\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE2 (Relax)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.594\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.613\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.579\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE3 (Pressure)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE4 (Drained)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.712\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.703\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.725\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE5 (Leisure)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.530\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.558\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE6 (Energised)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.785\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.791\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE7 (Weary)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.735\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.720\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.699\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE8 (Tired)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.585\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.589\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.593\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. Exploratory factor analysis was conducted using the maximum likelihood method with oblimin rotation. Factor pattern coefficients above 0.500 are in bold. Scree plots, parallel analysis, and minimum average partial correlation indicated that a 2-factor or 3-factor solution and a 2-factor solution could explain the data for the original and revised measures, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e near here]\u003c/h2\u003e\u003cp\u003eIn Model 1, the first and second factors corresponded to disengagement and exhaustion, respectively, while a third factor emerged with only one strong loading (E1). Items D5, D7, and E3 did not load substantially on any factor, and E6 loaded more strongly on disengagement than on exhaustion. In contrast, all items in Model 2 showed strong, exclusive loadings (\u0026ge;\u0026thinsp;0.55) on their respective factors. Fit statistics (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) supported the three-factor solution for Model 1 and the two-factor solution for Model 2. The 11-item OLBI-MS (Model 2) was selected for further analysis due to its clearer structure and superior model fit relative to the full 16-item version (see Supplementary Tables\u0026nbsp;1 and 2 in Additional files 3 and 4).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFit Statistics for Different OLBI-MS Measurement Models\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eRMSEA 90% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1 (16 items)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e203.431 (89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.471 (75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.972\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2 (11 items)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.863 (34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eCFI\u0026thinsp;=\u0026thinsp;Comparative fit index, RMSEA\u0026thinsp;=\u0026thinsp;Root mean squared error of approximation, SRMR\u0026thinsp;=\u0026thinsp;Standardised root mean square residual, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student, CI\u0026thinsp;=\u0026thinsp;Confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e near here]\u003c/h2\u003e\u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\u003ch2\u003eBifactor Model and Method Effects\u003c/h2\u003e\u003cp\u003eA bifactor model was estimated using the 11-item OLBI-MS to assess potential method effects from reverse-coded items. The model included one general factor (g), two group-specific factors (Disengagement and Exhaustion), and one orthogonal method factor. Fit indices indicated good fit: χ\u0026sup2;(26)\u0026thinsp;=\u0026thinsp;37.16, p\u0026thinsp;=\u0026thinsp;0.072, CFI\u0026thinsp;=\u0026thinsp;0.983, TLI\u0026thinsp;=\u0026thinsp;0.964, RMSEA\u0026thinsp;=\u0026thinsp;0.047, SRMR\u0026thinsp;=\u0026thinsp;0.037. Several items loaded significantly on both content and method factors, indicating that the bifactor model effectively accounted for method variance. Comparison with the standard two-factor model indicated improved fit (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Model Fit Indices Between the Two-Factor Model and the Bifactor Model of the 11-Item OLBI-MS\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRMSEA 90% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTwo-factor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.40 (43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4186.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4262.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBifactor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.156 (26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4165.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4296.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAIC\u0026thinsp;=\u0026thinsp;Akaike information criterion, BIC\u0026thinsp;=\u0026thinsp;Bayesian information criterion, CFI\u0026thinsp;=\u0026thinsp;Comparative fit index, RMSEA\u0026thinsp;=\u0026thinsp;Root mean squared error of approximation, SRMR\u0026thinsp;=\u0026thinsp;Standardised root mean square residual, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student,\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eCI\u0026thinsp;=\u0026thinsp;Confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e near here]\u003c/h2\u003e\u003cdiv id=\"Sec26\" class=\"Section4\"\u003e\u003ch2\u003eMeasurement Invariance across Gender\u003c/h2\u003e\u003cp\u003eMulti-group CFA assessed measurement invariance for the 11-item OLBI-MS across gender. The configural model showed acceptable fit (χ\u0026sup2;(86)\u0026thinsp;=\u0026thinsp;150.41, CFI\u0026thinsp;=\u0026thinsp;0.901, RMSEA\u0026thinsp;=\u0026thinsp;0.088), suggesting structural equivalence. Metric invariance was supported (Δχ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;4.75, p\u0026thinsp;=\u0026thinsp;0.856, CFI\u0026thinsp;=\u0026thinsp;0.907, RMSEA\u0026thinsp;=\u0026thinsp;0.081), and scalar invariance was also established (Δχ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;11.88, p\u0026thinsp;=\u0026thinsp;0.220, CFI\u0026thinsp;=\u0026thinsp;0.903, RMSEA\u0026thinsp;=\u0026thinsp;0.079). These results support full measurement invariance across gender (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eMeasurement Invariance Across Gender for the 11-item OLBI-MS (n\u0026thinsp;=\u0026thinsp;195)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e (df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eΔχ\u0026sup2;(df)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value (Δχ\u0026sup2;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRMSEA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eRMSEA 90% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConfigural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150.41 (86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.901\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e155.16 (95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.75 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScalar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167.04 (104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.88 (9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eΔχ\u0026sup2; and p-values represent chi-square difference tests comparing nested models. Configural invariance tests the same factor structure across groups; metric invariance constrains factor loadings to be equal; scalar invariance further constrains item intercepts. CFI\u0026thinsp;=\u0026thinsp;Comparative fit index, RMSEA\u0026thinsp;=\u0026thinsp;Root mean squared error of approximation, SRMR\u0026thinsp;=\u0026thinsp;Standardised root mean square residual, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student, CI\u0026thinsp;=\u0026thinsp;Confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e near here]\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section4\"\u003e\u003ch2\u003eInternal consistency and reliability\u003c/h2\u003e\u003cp\u003eInternal consistency for the 11-item OLBI-MS was acceptable to good (alpha\u0026thinsp;=\u0026thinsp;0.77\u0026ndash;0.83; omega\u0026thinsp;=\u0026thinsp;0.76\u0026ndash;0.83). Test\u0026ndash;retest reliability among 185 participants was strong (ICC\u0026thinsp;=\u0026thinsp;0.802\u0026ndash;0.845). Other scales demonstrated good internal consistency, except for the VQ\u0026ndash;Obstruction subscale (alpha\u0026thinsp;=\u0026thinsp;0.59), which was consistent with previous findings (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics and Reliability for Study Measures\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo. of Items\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCronbach\u0026rsquo;s alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMcDonald\u0026rsquo;s omega\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eICC \u003c/p\u003e\u003cp\u003e(Retest, n\u0026thinsp;=\u0026thinsp;185)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOLBI-MS-D \u003c/p\u003e\u003cp\u003e(11-item version)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.26 (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOLBI-MS-E \u003c/p\u003e\u003cp\u003e(11-item version)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.47 (0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.831\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBI-GS-EX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.85 (1.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBI-GS-CY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.68 (1.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMBI-GS-PE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.65 (1.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWAAQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.08 (6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVQ\u0026ndash;Progress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.62 (5.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVQ\u0026ndash;Obstruct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.57 (5.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24.23 (5.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.41 (3.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eCronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega were calculated to assess internal consistency. Test\u0026ndash;retest reliability was evaluated using intraclass correlation coefficients (ICC) in a subsample of participants who completed the OLBI-MS twice (n\u0026thinsp;=\u0026thinsp;185). A hyphen (\u0026ndash;) indicates that the coefficient was not applicable or not estimated in this study. D\u0026thinsp;=\u0026thinsp;Disengagement, E\u0026thinsp;=\u0026thinsp;Exhaustion, CY\u0026thinsp;=\u0026thinsp;Cynicism, EX\u0026thinsp;=\u0026thinsp;Exhaustion, PE\u0026thinsp;=\u0026thinsp;Personal efficacy, SD\u0026thinsp;=\u0026thinsp;Standard deviation, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student, MBI-GS\u0026thinsp;=\u0026thinsp;Maslach Burnout Inventory-General Survey, WAAQ\u0026thinsp;=\u0026thinsp;Work-related Acceptance and Action Questionnaire, VQ\u0026thinsp;=\u0026thinsp;Valuing Questionnaire, PDDS\u0026thinsp;=\u0026thinsp;Perceived Devaluation-Discrimination Scale, PHQ-9\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-9.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e near here]\u003c/h2\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003eHypotheses for construct validity\u003c/h2\u003e\u003cp\u003eAll variables showed acceptable skewness (\u0026lt;\u0026thinsp;2.0) and kurtosis (\u0026lt;\u0026thinsp;7.0), justifying use of Pearson correlations (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Hypothesis testing results were as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHypothesis testing for construct validity\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1 OLBI-MS-D \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(11 items)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2 OLBI-MS-E \u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(11 items)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3 MBI-GS-CY\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.61\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4 MBI-GS-EX\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.73\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5 MBI-GS-PE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-0.31\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e-0.17\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e-0.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6 WAAQ\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-0.25\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-0.34\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7 VQ-Progres\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-0.32\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-0.36\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8 VQ-Obsrtruct\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.30\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.29\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e-0.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9 PHQ-9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.32\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.59\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e-0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10 PDDs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.21\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e0.17\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e0.16\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e-0.18\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003e-0.14\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e0.18\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11 Mistreatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.37\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003e0.15\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003e0.18\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cb\u003eBold\u003c/b\u003e indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eitalic\u003c/em\u003e indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eUnderline\u003c/span\u003e indicates the relationships described in the hypotheses. D\u0026thinsp;=\u0026thinsp;Disengagement, E\u0026thinsp;=\u0026thinsp;Exhaustion, CY\u0026thinsp;=\u0026thinsp;Cynicism, EX\u0026thinsp;=\u0026thinsp;Exhaustion, PE\u0026thinsp;=\u0026thinsp;Personal efficacy, OLBI-MS\u0026thinsp;=\u0026thinsp;Oldenburg Burnout Inventory\u0026ndash;Medical Student, MBI-GS\u0026thinsp;=\u0026thinsp;Maslach Burnout Inventory-General Survey, WAAQ\u0026thinsp;=\u0026thinsp;Work-related Acceptance and Action Questionnaire, VQ\u0026thinsp;=\u0026thinsp;Valuing Questionnaire, PDDS\u0026thinsp;=\u0026thinsp;Perceived Devaluation-Discrimination Scale, PHQ-9\u0026thinsp;=\u0026thinsp;Patient Health Questionnaire-9\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e\u003cp\u003eDisengagement correlated more strongly with Cynicism (r\u0026thinsp;=\u0026thinsp;0.61) than with Exhaustion (r\u0026thinsp;=\u0026thinsp;0.38).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e\u003cp\u003eExhaustion correlated more strongly with MBI-GS Exhaustion (r\u0026thinsp;=\u0026thinsp;0.73) than with Cynicism (r\u0026thinsp;=\u0026thinsp;0.42).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e\u003cp\u003eBoth subscales were negatively correlated with Professional Efficacy (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.31 and \u0026minus;\u0026thinsp;0.17).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 4\u003c/strong\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e: Disengagement and Exhaustion were negatively associated with WAAQ (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25 and \u0026minus;\u0026thinsp;0.34), VQ\u0026ndash;Progress (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.32 and \u0026minus;\u0026thinsp;0.36), and positively with VQ\u0026ndash;Obstruction (r\u0026thinsp;=\u0026thinsp;0.30 and 0.29).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 7\u003c/strong\u003e\u003cp\u003ePHQ-9 scores correlated positively with both subscales (r\u0026thinsp;=\u0026thinsp;0.32 and 0.59).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 8\u003c/strong\u003e\u003cp\u003ePerceived stigma (PDDS) showed weak but positive correlations (r\u0026thinsp;=\u0026thinsp;0.21 and 0.17).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 9\u003c/strong\u003e\u003cp\u003eMistreatment correlated moderately with Disengagement (r\u0026thinsp;=\u0026thinsp;0.37) and weakly with Exhaustion (r\u0026thinsp;=\u0026thinsp;0.13), consistent with prior findings.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e near here]\u003c/h2\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe Japanese version of the 11-item OLBI-MS version demonstrated satisfactory psychometric properties, including internal consistency, test\u0026ndash;retest reliability, structural validity, and evidence for construct validity based on hypothesis testing. These findings support its use as a brief and robust tool for assessing burnout in medical students during clinical clerkships.\u003c/p\u003e\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e\u003ch2\u003eSociodemographic characteristics\u003c/h2\u003e\u003cp\u003eThis study achieved a high response rate from fifth- and sixth-year medical students at two universities. The participants\u0026rsquo; demographic characteristics, such as alcohol consumption, extracurricular activities, and cohabitation status, were similar to those reported in previous national studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The prevalence rates of exhaustion, cynicism, low professional efficacy, and severe burnout were also consistent with earlier findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eStructural validity\u003c/h2\u003e\u003cp\u003eThe results supported a two-factor structure for the revised 11-item OLBI-MS, with all items demonstrating factor loadings of 0.55 or above. In contrast, the two-factor model of the original 16-item version was not supported. This aligns with findings from a previous study involving second-year medical students in the United States, which similarly failed to validate the two-factor structure of the 16-item scale [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. A 10-item two-factor model, nearly identical to the present 11-item version except for the exclusion of item D6 (sickened), was proposed in that study. However, the current 11-item version exhibited superior model fit, with stronger item loadings, particularly for D2 (talk) and D3 (think), which had loadings below 0.5 in the 10-item model. This improvement may reflect refinements in item wording during translation, enhancing coherence and clarity. Additionally, the present sample comprised clinical-year students with greater clinical exposure than the preclinical second-year cohort used in the earlier study.\u003c/p\u003e\u003cp\u003eThe 16-item three-factor model in the current study demonstrated satisfactory fit, yet item E1 (manage) loaded significantly on a third factor not clearly aligned with self-efficacy, a burnout dimension defined by the World Health Organization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This pattern was also observed in Runyon et al. (2022). Furthermore, items D5 (disconnected), D7 (imagine), and E3 (pressure) exhibited weak loadings across both the two- and three-factor solutions. By contrast, E6 (energised) consistently loaded on disengagement rather than exhaustion, a finding consistent with previous validation work on the English-language OLBI [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Taken together, these findings support the revised 11-item version as a more structurally valid representation of the OLBI\u0026rsquo;s intended two-factor model of burnout\u0026mdash;disengagement and exhaustion\u0026mdash;compared to the original 16-item form.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBifactor model and method effects\u003c/h3\u003e\n\u003cp\u003eThe bifactor model revealed good fit indices and supported the presence of a general burnout factor alongside domain-specific (Disengagement and Exhaustion) and method-related (item wording) factors. This model outperformed the standard two-factor model and highlighted the impact of reverse-worded items on measurement variance.\u003c/p\u003e\u003cp\u003eThese findings imply that burnout symptoms among medical students may be understood as comprising both a general latent tendency (e.g., overall burnout severity) and two domain-specific expressions (disengagement and exhaustion), with reverse-worded items contributing additional methodological variance. This highlights the importance of accounting for method effects when interpreting multidimensional burnout scales such as the OLBI. The results are consistent with previous research indicating that bifactor models can improve construct validity by isolating artifactual variance due to item wording [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Incorporating a method factor helped clarify the substantive structure of the OLBI-MS and supports its psychometric robustness in the Japanese context.\u003c/p\u003e\n\u003ch3\u003eMeasurement invariance across gender\u003c/h3\u003e\n\u003cp\u003eThis study demonstrated that the Japanese version of the 11-item OLBI-MS exhibited full measurement invariance across gender groups. Specifically, configural, metric, and scalar invariance were supported, indicating that the factor structure, factor loadings, and item intercepts were equivalent for male and female medical students. These findings suggest that the OLBI-MS assesses the constructs of disengagement and exhaustion consistently regardless of gender, allowing for meaningful comparisons of latent burnout levels between male and female groups. Invariance testing is critical for ensuring that any observed differences in burnout scores between groups reflect true differences in the underlying construct rather than measurement artifacts.\u003c/p\u003e\u003cp\u003eThe establishment of scalar invariance is particularly noteworthy, as it justifies the use of observed mean scores to compare burnout levels across gender without bias. Previous studies have raised concerns about gender-related response styles in burnout measurement tools, especially when reverse-coded items are involved [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. However, the use of bifactor modeling and subsequent invariance testing in this study provides reassurance that the Japanese OLBI-MS functions similarly across genders even in the presence of mixed item wordings.\u003c/p\u003e\u003cp\u003eFurthermore, the ability to validly compare burnout symptoms between male and female medical students is especially important in Japan, where gender-related differences in stress, expectations, and mistreatment during clinical training have been documented [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The present results support the OLBI-MS as a gender-equitable instrument in this context.\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section2\"\u003e\u003ch2\u003eInternal consistency and reliability\u003c/h2\u003e\u003cp\u003eBoth subscales of the OLBI-MS demonstrated acceptable to good internal consistency (alpha and omega\u0026thinsp;\u0026ge;\u0026thinsp;0.77) and strong test\u0026ndash;retest reliability (ICC\u0026thinsp;=\u0026thinsp;0.80\u0026ndash;0.85). These results meet standard psychometric criteria and align with previous validation studies in other languages [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The results also highlight the advantages of using model-based reliability indices such as McDonald's omega in conjunction with Cronbach\u0026rsquo;s alpha. While alpha assumes equal factor loadings and uncorrelated errors, omega provides a more realistic estimate of reliability under a latent variable framework, especially for multidimensional instruments like the OLBI-MS [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, the VQ\u0026ndash;Obstruction subscale showed lower internal consistency (alpha\u0026thinsp;=\u0026thinsp;0.59), a finding consistent with past Japanese research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Interpretations involving this measure should therefore be made with caution.\u003c/p\u003e\u003cdiv id=\"Sec38\" class=\"Section3\"\u003e\u003ch2\u003eHypothesis testing for construct validity\u003c/h2\u003e\u003cp\u003eThe predefined hypotheses were generally supported, providing robust evidence for the construct validity of the Japanese OLBI-MS. Consistent with expectations, the Disengagement subscale showed a strong positive correlation with Cynicism from the MBI-GS (r\u0026thinsp;=\u0026thinsp;0.61), while the Exhaustion subscale correlated strongly with MBI-GS Exhaustion (r\u0026thinsp;=\u0026thinsp;0.73), confirming Hypotheses 1 and 2. Both subscales were also inversely associated with Professional Efficacy (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.31 and \u0026minus;\u0026thinsp;0.17), supporting Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and aligning with prior work [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRegarding psychological flexibility, both subscales were negatively correlated with WAAQ scores (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.25 and \u0026minus;\u0026thinsp;0.34), consistent with Hypothesis \u003cspan refid=\"FPar11\" class=\"InternalRef\"\u003e4\u003c/span\u003e. In line with Hypotheses 5 and 6, Disengagement and Exhaustion were negatively associated with value progress (VQ\u0026ndash;P) and positively with value obstruction (VQ\u0026ndash;O). These findings suggest that decreased value-oriented action and lower psychological flexibility are closely linked to burnout, as demonstrated in previous studies with medical students [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSupporting Hypothesis \u003cspan refid=\"FPar5\" class=\"InternalRef\"\u003e7\u003c/span\u003e, depressive symptoms measured via PHQ-9 were moderately correlated with Disengagement (r\u0026thinsp;=\u0026thinsp;0.32) and strongly with Exhaustion (r\u0026thinsp;=\u0026thinsp;0.59), corroborating earlier evidence that positions emotional exhaustion as a shared core of burnout and depression [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Disengagement may serve to delineate overlapping yet distinct symptomatology [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 8\u003c/strong\u003e\u003cp\u003ewas modestly supported, with weak positive correlations observed between both OLBI-MS subscales and perceived mental health stigma (r\u0026thinsp;=\u0026thinsp;0.21 and 0.17), consistent with reports linking burnout with elevated stigma perceptions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis 9\u003c/strong\u003e\u003cp\u003ereceived partial support. Mistreatment experiences showed a moderate correlation with Disengagement (r\u0026thinsp;=\u0026thinsp;0.37) but only a weak association with Exhaustion (r\u0026thinsp;=\u0026thinsp;0.13), in line with prior findings highlighting interpersonal mistreatment as a driver of attitudinal withdrawal rather than emotional depletion[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003eOverall, the results affirm the construct validity of the Japanese OLBI-MS by demonstrating meaningful associations across affective, behavioural, and interpersonal domains relevant to burnout.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec39\" class=\"Section2\"\u003e\u003ch2\u003eStrengths, limitations, and implications\u003c/h2\u003e\u003cp\u003eThis study presents several notable strengths. It followed a rigorous methodological framework in line with the latest COSMIN guidelines [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], enabling a comprehensive assessment of the scale\u0026rsquo;s psychometric properties. The sample was drawn from two geographically and institutionally diverse university hospitals that implement clinical clerkship models reflective of Japan\u0026rsquo;s evolving medical education system. By targeting fifth- and sixth-year students actively involved in clinical care, the study captured the relevant contextual stressors and psychological demands associated with burnout in authentic settings.\u003c/p\u003e\u003cp\u003eNonetheless, the study has limitations. First, the scale\u0026rsquo;s responsiveness\u0026mdash;its ability to detect changes over time\u0026mdash;was not assessed. Second, although the translation process adhered to COSMIN standards, including expert review and back-translation, formal testing of cross-cultural validity was not conducted. Third, while measurement invariance by gender was established, the findings may not generalise to preclinical students or those outside clinical training contexts.\u003c/p\u003e\u003cp\u003eImportantly, the study coincided with a transformative period in Japan\u0026rsquo;s medical education, marked by a national shift from passive observation to active team-based clinical clerkships, prompted by the ECFMG\u0026rsquo;s 2023 Accreditation policy [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. This reform aligns Japanese training with international benchmarks and underscores the need for culturally validated, globally relevant burnout measures. The Japanese OLBI-MS, as validated here, addresses this need by offering a concise, psychometrically robust instrument suitable for use in clinical education settings. It provides a practical foundation for cross-national research and targeted intervention development within Japan\u0026rsquo;s changing medical training landscape.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe Japanese version of the OLBI-MS demonstrated sound psychometric properties, including structural validity, internal consistency, test\u0026ndash;retest reliability, measurement invariance across gender, and construct validity supported by hypothesis testing. The shortened 11-item version, characterised by a clearly defined two-factor structure (Disengagement and Exhaustion), exhibited superior model fit and interpretability compared to the original 16-item version. Bifactor modelling further supported the presence of both substantive and method-related variance, highlighting the relevance of accounting for reverse-worded item effects in burnout measurement. Additionally, the study established significant associations between burnout and related psychological constructs, including depression, psychological flexibility, stigma, and mistreatment experiences. These findings support the Japanese OLBI-MS as a theoretically grounded, culturally adapted, and psychometrically robust instrument for assessing burnout among medical students participating in clinical clerkships. The 11-item version offers a practical and efficient tool for screening and tracking burnout in medical education. Further research is recommended to evaluate its generalisability to other educational levels, health professions, and cultural contexts. Future studies should also examine its longitudinal validity, particularly its sensitivity to change, to inform its use in intervention research and well-being promotion.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAQ-2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcceptance and Action Questionnaire\u0026ndash;II\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComparative Fit Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfirmatory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConservation of Resources (theory)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCynicism (subscale of MBI-GS)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003edf\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDegrees of freedom\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECFMG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEducational Commission for Foreign Medical Graduates\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExploratory Factor Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEX\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eExhaustion (subscale of MBI-GS)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntraclass Correlation Coefficient\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJD\u0026ndash;R\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eJob Demands\u0026ndash;Resources (model)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKMO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKaiser\u0026ndash;Meyer\u0026ndash;Olkin (measure of sampling adequacy)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMinimum Average Partial (test)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMBI-GS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMaslach Burnout Inventory\u0026ndash;General Survey\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOLBI-MS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOldenburg Burnout Inventory\u0026ndash;Medical Student\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePDDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePerceived Devaluation\u0026ndash;Discrimination Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProfessional Efficacy (subscale of MBI-GS)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePHQ-9\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRMSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRoot Mean Square Error of Approximation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSRMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandardized Root Mean Square Residual\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTLI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTucker\u0026ndash;Lewis Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUMIN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUniversity Hospital Medical Information Network (Clinical Trials Registry)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eValuing Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVQ-O\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eValuing Questionnaire \u0026ndash; Obstruction (subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVQ-P\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eValuing Questionnaire \u0026ndash; Progress (subscale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWAAQ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWork-related Acceptance and Action Questionnaire\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Nagoya City University Graduate School of Medical Sciences (Approval No.: 60-22-0087) and registered with the University Hospital Medical Information (UMIN) Clinical Trials Registry (UMIN000048572), registered on 4 August 2022, prior to commencement. The study was conducted in accordance with the Declaration of Helsinki. All participants received written and verbal information about the study and provided informed consent electronically by checking the consent box before participation. All participants received written and verbal information about the study before beginning their psychiatric clinical clerkship. They were assured that not participating would not impact their grades and that their privacy would be protected. Participants were given time to consider participation. Those who agreed accessed the Google Form online and checked the consent box.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Supplementary materials are provided as Additional files 1\u0026ndash;4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTW was supported in this research by Grant-in-Aid for Scientific Research (JP22K10420) from the Japan Society for the Promotion of Science (https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-22K10420/). The funders had no involvement in the design of this study, the collection, analysis, and interpretation of the data, or in the writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTW, TN, NS, and MK conceptualised and designed the study. The data were acquired by TW, IO, MK, and OT. The analysis and interpretation of the data was conducted by TW, MS, and AT. TW wrote the manuscript. OT and AK made significant contributions to the revision of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the medical students at Nagoya City University Medical School and the University of Fukui School of Medical Sciences for participating in this study, as well as Ms. Kaori Kobori, Secretary at the Department of Psychiatry, Nagoya City University Graduate School of Medical Sciences, for her help. We also thank to Editage (www.editage.com) for English language editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. International classification of diseases 11th revision (ICD-11). [Internet]. 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[Internet]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ecfmg.org/accreditation/\u003c/span\u003e\u003cspan address=\"https://www.ecfmg.org/accreditation/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 23 Sep 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"medical student, burnout, clinical clerkship, Oldenburg Burnout Inventory, reliability, validity","lastPublishedDoi":"10.21203/rs.3.rs-7717852/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7717852/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBurnout, characterised by exhaustion, disengagement, and diminished professional efficacy, represents a significant concern in medical education, particularly during clinical training. Although the construct has been extensively studied worldwide, a validated Japanese version of the Oldenburg Burnout Inventory for Medical Students (OLBI-MS) was previously unavailable.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eFollowing the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN), we translated and culturally adapted the OLBI-MS into Japanese. A cross-sectional survey was conducted among 195 fifth- and sixth-year medical students during psychiatry clinical clerkships at two Japanese university hospitals. Participants completed an online survey comprising the Japanese OLBI-MS and other established instruments: the Maslach Burnout Inventory\u0026ndash;General Survey, Work-related Acceptance and Action Questionnaire, Valuing Questionnaire, Perceived Devaluation\u0026ndash;Discrimination Scale, Patient Health Questionnaire-9, and a mistreatment measure. We examined internal consistency (Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s omega), test\u0026ndash;retest reliability (intraclass correlation), and structural validity (confirmatory and exploratory factor analyses, and bifactor modelling). Measurement invariance by gender and hypothesis-based construct validity (via Pearson correlations) were also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe original 16-item version exhibited poor model fit. The refined 11-item version demonstrated a robust two-factor structure (Exhaustion and Disengagement) and acceptable fit across exploratory and bifactor models. The instrument showed strong internal consistency (alpha and omega\u0026thinsp;\u0026ge;\u0026thinsp;0.77), high test\u0026ndash;retest reliability (ICC\u0026thinsp;\u0026ge;\u0026thinsp;0.80), and confirmed gender invariance. Construct validity was supported through expected correlations with related psychological measures.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe Japanese OLBI-MS is a psychometrically sound and culturally suitable instrument for assessing burnout among medical students in clinical training. The 11-item version offers a practical tool for ongoing assessment and may facilitate cross-cultural and longitudinal research on medical student well-being.\u003c/p\u003e","manuscriptTitle":"Validity and Reliability of a Japanese Version of the Oldenburg Burnout Inventory–Medical Student: A Study on Students in Clinical Clerkships","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 06:00:22","doi":"10.21203/rs.3.rs-7717852/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-14T09:57:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T09:18:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-20T07:06:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T08:16:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-10-18T08:13:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da7df560-dc43-4cec-9405-26d1d477ea49","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T10:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 06:00:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7717852","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7717852","identity":"rs-7717852","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}