Psychosocial work factors and social inequalities in psychological distress: a population-based study
Bibliographic record
Abstract
BACKGROUND: Mental health problems (MHP) are the leading cause of disability worldwide. The inverse association between socioeconomic position (SEP) and MHP has been well documented. There is prospective evidence that factors from the work environment, including adverse psychosocial work factors, could contribute to the development of MHP including psychological distress. However, the contribution of psychosocial work factors to social inequalities in MHP remains unclear. This study evaluates the contribution of psychosocial work factors from two highly supported models, the Demand-Control-Support (DCS) and the Effort-Reward Imbalance (ERI) models to SEP inequalities of psychological distress in men and women from a population-based sample of Quebec workers. METHODS: Data were collected during a survey on working conditions, health and safety at work. SEP was evaluated using education, occupation and household income. Psychosocial work factors and psychological distress were assessed using validated instruments. Mean differences (MD) in the score of psychological distress were estimated separately for men and women. RESULTS: Low education level and low household income were associated with psychological distress among men (MD, 0.56 (95% CI 0.06; 1.05) and 1.26 (95% CI 0.79; 1.73) respectively). In men, the contribution of psychosocial work factors from the DCS and the ERI models to the association between household income and psychological distress ranged from 9% to 24%. No clear inequalities were observed among women. CONCLUSIONS: These results suggest that psychosocial work factors from the DCS and the ERI models contribute to explain a part of social inequalities in psychological distress among men. Psychosocial factors at work are frequent and modifiable. The present study supports the relevance of targeting these factors for the primary prevention of MHP and for health policies aiming to reduce social inequalities in mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".