Reducing work-related stress among health professionals by using a training-based intervention programme for leaders in a cluster randomised controlled trial
Bibliographic record
Abstract
Healthcare organisations worldwide are affected by the shortage of health professionals due to work-related stress and health professional leaders play an important role by implementing effective strategies. Therefore, this study aims to investigate whether the STRAIN intervention program (using evidence-based training for health professional leaders) can reduce work-related stress among health professionals. This study is based on a cluster randomised controlled trial, consists of three measurements and includes 165 participating hospitals, nursing homes and home care organisations. A total of 206 health professional leaders took part in the intervention programme and 19,340 health professionals participated in the study. Results showed no significant differences (p > 0.05) between the intervention and control group regarding the effort-reward imbalance ratio, quantitative demands, opportunities for development, bond with the organisation, quality of leadership, social community, role clarity, rewards, difficulties with demarcation and work-private life conflict. Pre-/post-test analysis revealed a tendency for significant positive results (p < 0.05) for stressors, stress symptoms and long-term consequences for organisations with a leaders' participation rate of ≥ 75%. Leaders' awareness, commitment and readiness is essential to implement effective strategies reducing work-related stress.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".