Reciprocal relations between care-related emotional burden and sleep problems in healthcare professionals: a multicentre international cohort study
Bibliographic record
Abstract
Objective To determine whether there are reciprocal relations between care-related regret and insomnia severity among healthcare professionals, and whether the use of different coping strategies influences these associations. Methods This is a multicentre international cohort study of 151 healthcare professionals working in acute care hospitals and clinics (87.4% female; mean age=30.4±8.0 years, 27.2% physicians, 48.3% nurses and 24.5% other professions) between 2014 and 2017. Weekly measures of regret intensity, number of regrets, and use of coping strategies (Regret Coping Scale) and sleep problems (Insomnia Severity Index) were assessed using a web survey. Results The associations between regret and insomnia severity were bidirectional. In a given week, regret intensity (bregret intensity→sleep=0.26, 95% credible interval (CI) (0.14 to 0.40)) and number of regrets (bnumber of regrets→sleep=0.43, 95% CI (0.07 to 0.53)) were significantly associated with increased insomnia severity the following week. Conversely, insomnia severity in a given week was significantly associated with higher regret intensity (bsleep→regret intensity=0.14, 95% CI (0.11 to 0.30)) and more regrets (bsleep→number of regrets=0.04, 95% CI (0.02 to 0.06)) the week after. The effects of regret on insomnia severity were much stronger than those in the opposite direction. The use of coping strategies, especially if they were maladaptive, modified the strength of these cross-lagged associations. Conclusions The present study showed that care-related regret and sleep problems are closely intertwined among healthcare professionals. Given the high prevalence of these issues, our findings call for the implementation of interventions that are specifically designed to help healthcare professionals to reduce their use of maladaptive coping strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".