Associations of regrets and coping strategies with job satisfaction and turnover intention: international prospective cohort study of novice healthcare professionals
Bibliographic record
Abstract
AIMS OF THE STUDY: (1) To assess the associations of care-related regrets with job satisfaction and turnover intention; and (2) to examine whether these associations are partially mediated by coping strategies. METHODS: Data came from ICARUS, a prospective international cohort study of novice healthcare professionals working in acute care hospitals and clinics from various countries (e.g., Australia, Austria, Botswana, Canada, Denmark, France, Haiti, Ireland, Kenya, the United Kingdom and United States). Care-related regrets (number of regrets and regret intensity), coping strategies, job satisfaction and turnover intention were assessed weekly for 1 year. RESULTS: 229 young healthcare professionals (2387 observations) were included in the analysis. For a given week, experiencing a larger number of care-related regrets was associated with decreased job satisfaction, and experiencing more intense care-related regrets was associated with increased turnover intention. These associations were partially mediated by coping strategies. Maladaptive emotion-focused strategies were associated with decreased job satisfaction and increased turnover intention, whereas adaptive problem-focused strategies showed the opposite pattern. CONCLUSIONS: Our results revealed that care-related regrets and maladaptive coping strategies are associated with job dissatisfaction and the intention to quit patient care. Helping healthcare professionals to cope with these emotional experiences seems essential to prevent early job quitting. .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".