Classic and emergent psychosocial work factors and mental health
Bibliographic record
Abstract
BACKGROUND: Little is known about associations between emergent psychosocial work factors and mental health. AIMS: To explore associations between classical and emergent psychosocial work factors and depression and anxiety symptoms in employees in France. METHODS: A national cross-sectional study (the SUrveillance Médicale des Expositions aux Risques professionnels (SUMER) survey) assessed psychosocial work factors including psychological demands, decision latitude, social support, reward and its sub-dimensions (esteem, job security and job promotion), bullying, verbal abuse, physical violence and sexual assault, long working hours, shift and night work, unsociable work days, predictability and demands for responsibility. We also measured depression and anxiety symptoms using the Hospital Anxiety and Depression scale. We used gender-stratified generalized linear models to adjust for age, occupation and economic activity. RESULTS: A total of 26883 men and 20079 women participated (response rate 87%). Low decision latitude, high psychological demands, low social support, low reward, bullying and verbal abuse were associated with depression and anxiety in both genders (β coefficients from 0.14 to 1.40). In men, low predictability was associated with both depression and anxiety (β = 0.12 [95% confidence interval (CI) 0.01, 0.24] and 0.19 [95% CI 0.06, 0.32]) and long working hours were associated with anxiety (β = 0.48 [95% CI 0.27, 0.69]). The strongest associations were observed for bullying, reward (especially esteem) and psychological demands. Using a less conservative approach, we found more factors to be significantly associated with mental health symptoms. CONCLUSIONS: Most psychosocial work factors studied are associated with depression and/or anxiety symptoms. Comprehensive prevention policies may help to reduce exposure to psychosocial work factors, including emergent ones, and improve mental health at work.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".