The determinants of burnout and professional turnover intentions among Canadian physicians: application of the job demands-resources model
Bibliographic record
Abstract
BACKGROUND: Burnout among physicians is growing at an exponential rate and many are leaving the profession. Nevertheless, the specific antecedents and intermediary stages involved in predicting their professional turnover intentions are not fully clear. PURPOSE: We apply the Job Demands-Resources model and investigate an innovative model which predicts physician burnout and its ultimate consequences on professional turnover intentions. METHODOLOGY/APPROACH: Structural equation modeling was used on cross-sectional survey data from a sample of 407 Canadian physicians. RESULTS/CONCLUSIONS: Job demands (work stress, work overload, and work-family conflict) and job resources (patient recognition and meaning at work) influence intention to leave the profession through a two stage health-impairment and motivational process related to health problems and professional commitment, respectively. PRACTICAL IMPLICATIONS: This study identifies key job resources and job demands which predict physician burnout and professional turnover intentions thereby pinpointing which levers managers can use improve their health and retain them in the profession.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".