Validation of a 15‐item Care‐related Regret Coping Scale for Health‐care Professionals (RCS‐HCP)
Bibliographic record
Abstract
OBJECTIVES: Coping with difficult care-related situations is a common challenge for health-care professionals. How these professionals deal with the regrets they may experience following one of the many decisions and interventions they must make every day can have an impact on their own health and quality of life, and also on their patient care practices. To identify professionals most at need for extra support, development and validation of a tool measuring coping style are needed. METHODS: We performed a survey of physicians and nurses of a French-speaking University hospital; 469 health-care professionals responded to the survey, and 175 responded to the same survey one-month later. Regret was assessed with the regret coping scale developed for this study, self-report questions on the frequency of regretted situations and the intensity of regret. Construct validity was assessed using measures of health-care professionals' quality of life (including job and life satisfaction, and self-reported health) as well as sleep problems and depression. RESULTS: Based on factor analysis and item response analysis, the initial 31-item scale was shortened to 15 items, which measured three types of strategies: problem-focused strategies (i.e., trying to find solutions, talking to colleagues) and two types of emotion-focused strategies, A (i.e., self-blame, rumination) and B (e.g., acceptance, emotional distance). All subscales showed high internal consistency (α >0.85). Overall, as expected, problem-focused and emotion-focused B strategies correlated with higher quality of life, fewer sleep problems and less depression, and emotion-focused A strategies showed the opposite pattern. CONCLUSIONS: The regret coping scale (RCS-HCP) is a valid and reliable measure of coping abilities of hospital-based health-care professionals.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".