Psychosocial and organizational work environment of nurse managers and self-reported depressive symptoms: Cross-sectional analysis from a cohort of nurse managers
Bibliographic record
Abstract
OBJECTIVES: The association between depressive symptoms and psycho-organisational work environment has been established in the literature. Some studies have evaluated depressive symptoms in healthcare workers, but little research has been carried out among nurse managers. The aim of the study is to evaluate the depressive symptoms prevalence among nurse managers' population and work environment factors. MATERIAL AND METHODS: A descriptive correlational research design was used. Data were collected from 296 nurse managers in five hospitals in the eastern area of France between 2007 and 2008. Health outcomes were evaluated by measuring depressive symptoms (CES-D scale), the exposure data by assessing psycho-organisational work environment with effort-reward imbalance-model of Siegrist. Multiple logistic regressions were used to describe the strength of the association between depressive symptoms and effort-reward imbalance adjusted for personal and occupational characteristics of the nurse managers. RESULTS: Among the nurse managers, a third had depressive symptoms, and 18% presented an effort-reward imbalance (ratio: ≥ 1). A significant association was found between depressive symptoms and effort-reward imbalance (OR = 10.81, 95% CI: 5.1-23, p < 10(-3)), and with esteem as a reward (OR = 3.21, 95% CI: 1.6-6.3, p < 10(-2)). CONCLUSION: In view of the hierarchical situation of nurse managers and their primary roles in hospitals, it is necessary to take prevention measures to improve their work environment and health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".