Update of the fractions of cardiovascular diseases and mental disorders attributable to psychosocial work factors in Europe
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to provide the fractions of cardiovascular diseases and mental disorders attributable to five psychosocial work exposures, i.e. job strain, effort-reward imbalance, job insecurity, long working hours, and bullying in Europe (35 countries, including 28 European Union countries), for each one and all countries together, in 2015. METHODS: The prevalences of exposure were estimated using the sample of 35,571 employees from the 2015 European Working Conditions Survey (EWCS) for all countries together and each country separately. Relative risks (RR) were obtained via literature reviews and meta-analyses already published. The studied outcomes were: coronary/ischemic heart diseases (CHD), stroke, atrial fibrillation, peripheral artery disease, venous thromboembolism, and depression. Attributable fractions (AF) for each exposure and overall AFs for all exposures together were calculated. RESULTS: The AFs of depression were all significant: job strain (17%), job insecurity (9%), bullying (7%), and effort-reward imbalance (6%). Most of the AFs of cardiovascular diseases were significant and lower than 11%. Differences in AFs were observed between countries for depression and for long working hours. Differences between genders were found for long working hours, with higher AFs observed among men than among women for all outcomes. Overall AFs taking all exposures into account ranged between 17 and 35% for depression and between 5 and 11% for CHD. CONCLUSION: The overall burden of depression and cardiovascular diseases attributable to psychosocial work exposures was noticeable. As these exposures are modifiable, preventive policies may be useful to reduce the burden of disease associated with the psychosocial work environment.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".