Improving the psychosocial work environment
Bibliographic record
Abstract
AIM: This paper reports a pilot project to evaluate the effectiveness of a participatory organizational intervention to improve the psychosocial work environment in one long-term care unit. BACKGROUND: Since the early 1990s, health care institutions in Quebec have been constantly changing in order to improve the efficiency of the health care system. These changes have affected the work environment and have contributed to higher rates of burnout and absenteeism among nurses and other health care workers. METHOD: The study participants were health care workers in a long-term care unit (n = 60). The participatory organizational intervention was based on a contract and carried out by a work team. Work constraints were identified, and an action plan implemented, The effectiveness of the intervention was evaluated by pre- and postintervention questionnaires: the Job Content Questionnaire, Effort-Reward Imbalance Questionnaire and Psychiatric Symptom Index. RESULTS: There was a significant increase in reward (P < or = 0.01) and a significant decrease in Effort-Reward Imbalance (P < or = 0.01) following the intervention. Absenteeism rates decreased from 8.26% to 1.86% over the study period, but in the rest of the institution remained the same. However, there was a significant decrease in social support from supervisors (P < 0.05) at post-test. CONCLUSION: Participation by health care workers and action plans targeting problematic aspects of the psychosocial work environment are key elements in interventions to improve their health. However, such interventions present challenges, such as the involvement of managers, involvement of all relevant participants, and re-establishment of trust within work teams. Recognition and respect must be re-established, and supervisors must engage with health care workers and give support at all stages of the intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".