Clinical teaching self-efficacy positively predicts professional fulfillment and negatively predicts burnout amongst Thai physicians: a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Clinician teachers (physicians who teach in clinical settings) experience considerable psychological challenges in providing both educational training and patient care. This study aimed to determine the prevalence of physician burnout and professional fulfillment, and to identify internal and external factors associated with mental health outcomes among Thai clinician teachers working in non-university teaching hospitals. METHOD: A one-time online questionnaire was completed by physicians at 37 governmental, non-university teaching hospitals in Thailand, with 227 respondents being assessed in the main analyses. Four outcomes were evaluated including burnout, professional fulfillment, quality of life, and intentions to quit. RESULTS: The observed prevalence of professional fulfillment was 20%, and burnout was 30.7%. Hierarchical regression analysis showed a significant internal, psychological predictor (clinical teaching self-efficacy) and external, structural predictors (multiple roles at work, teaching support), controlling for the background variables of gender, years of teaching experience, family roles, and active chronic disease, with clinical teaching self-efficacy positively predicting professional fulfillment (b = 0.29, p ≤.001) and negatively predicting burnout (b = - 0.21, p =.003). CONCLUSIONS: Results highlight the importance of faculty development initiatives to enhance clinical teaching self-efficacy and promote mental health among Thai physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".