The effect of work stress on turnover intention amongst family doctors: A conditional process analysis
Bibliographic record
Abstract
OBJECTIVE: To explore describe the mechanism between work stress, job satisfaction, perceived organizational support and turnover intention amongst family doctors in China, and to provide reference for improving the brain drain of family doctors. METHOD: Using convenience sampling, a questionnaire survey was conducted among 2358 family doctors in 13 provinces in eastern, central and western China. Pearson correlation analysis and hierarchical regression analysis were used to explore the effects of job stress, job satisfaction and perceived organizational support on family doctors' turnover intention. RESULTS: Family doctors' work stress was positively correlated with turnover intention (β = 0.631, p＜0.001), job satisfaction played a partial mediating role in the influence path of work stress on turnover intention (β = 0.9175, p＜0.001), perceived organizational support played a moderating role in the relationship between work stress, job satisfaction, and turnover intention (β = 0.124, p＜0.05; β = -0.022, p＜0.05). CONCLUSION: Hospital managers should take corresponding measures to reduce the work stress of family doctors, improve their job satisfaction, and provide more support and attention to family doctors to ensure the stable development of family doctors in China.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".