Contribution of the Psychosocial Work Environment to Psychological Distress Among Health Care Professionals Before and During a Major Organizational Change
Bibliographic record
Abstract
The aim of this study was to investigate the relationships between 4 dimensions of the psychosocial work environment (psychological demands, decision latitude, social support, and effort-reward) among health care professionals as well as their psychological distress during a reorganization process. A correlational descriptive design was used for this quantitative study. A total of 159 health care professionals completed the questionnaire at T1, and 141 at T2. First, before the work reorganization, effort-reward imbalance was the sole variable of the psychological work environment that significantly predicted psychological distress. Second, the high overall level of psychological distress increased during the process of organizational change (from T1 to T2). Finally, effort-reward imbalance, high psychological demands, and low decision latitude were all significant predictors of psychological distress at T2, during the organizational change. In conclusion, to reduce the expected negative outcomes of restructuring on health care practitioners, managers could increase the number of opportunities for rewards, carefully explain the demands, and clarify the tasks to be performed by each of the employees to reduce their psychological burden and increase their perceptions of autonomy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".