Development and implementation of a participative intervention to improve the psychosocial work environment and mental health in an acute care hospital
Bibliographic record
Abstract
OBJECTIVES: To describe the development and implementation phases of a participative intervention aimed at reducing four theory grounded and empirically supported adverse psychosocial work factors (high psychological demands, low decision latitude, low social support, and low reward), and their mental health effects. METHODS: The intervention was realised among 500 care providers in an acute care hospital. A prior risk evaluation was performed, using a quantitative approach, to determine the prevalence of adverse psychosocial work factors and of psychological distress in the hospital compared to an appropriate reference population. In addition, a qualitative approach included observation in the care units, interviews with key informants, and collaborative work with an intervention team (IT) including all stakeholders. RESULTS: The prior risk evaluation showed a high prevalence of adverse psychosocial factors and psychological distress among care providers compared to a representative sample of workers from the general population. Psychosocial variables at work associated with psychological distress in the prior risk evaluation were high psychological demands (prevalence ratio (PR) = 2.27), low social support from supervisors and co-workers (PR = 1.35), low reward (PR = 2.92), and effort-reward imbalance (PR = 2.65). These results showed the empirical relevance of an intervention on the four selected adverse psychosocial factors among care providers. Qualitative methods permitted the identification of 56 adverse conditions and of their solutions. Targets of intervention were related to team work and team spirit, staffing processes, work organisation, training, communication, and ergonomy. CONCLUSION: This study adds to the scarce literature describing the development and implementation of preventive intervention aimed at reducing psychosocial factors at work and their health effects. Even if adverse conditions in the psychosocial environment and solutions identified in this study may be specific to the healthcare sector, the intervention process used (participative problem solving) appears highly exportable to other work organisations.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".