Burnout in Japanese Internists and Primary Care Physicians in 2024: The Prevalence and Risk Factors Using Structural Equation Modeling
Bibliographic record
Abstract
Objective To investigate the prevalence of burnout and identify its associated risk factors using structural equation modeling (SEM). Patients A cross-sectional web-based survey targeting members of the American College of Physicians Japan Chapter (ACP-JC) was conducted in March 2024. The survey included the Mini Z 2.0 for burnout assessment, Emotional Vulnerability Scale, and Japanese version of the Brief Resilience Scale (BRS-J). Descriptive statistics were used to analyze the demographic and workplace characteristics, and SEM with maximum likelihood estimation was employed to explore the relationships between resilience, emotional vulnerability, teamwork, and burnout. Results Of the 1,066 invited physicians, 103 (9.7%) responded to the survey. Burnout symptoms were reported by 26.2% of the participants. An SEM analysis indicated significant negative associations between resilience (standardized coefficient: -0.29, p=0.007), teamwork (standardized coefficient: -0.32, p<0.001), and burnout, whereas emotional vulnerability showed no significant associations (standardized coefficient: 0.05, p=0.630). Conclusion Approximately one in four Japanese internists and primary care physicians reported burnout symptoms. Resilience and teamwork have emerged as key protective factors, thus underscoring the importance of fostering supportive workplace environments. Therefore, interventions to enhance resilience and strengthen workplace support systems are recommended to mitigate burnout.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".