PSYCHOSOCIAL STRESSORS AT WORK AND ATRIAL FIBRILLATION INCIDENCE: A 18-YEAR PROSPECTIVE STUDY
Bibliographic record
Abstract
Objective: Psychosocial stressors at work are generally defined according to the job strain and the effort-reward imbalance at work (ERI) models. These work stressors were shown to increase coronary heart disease risk, with a higher risk among workers exposed to both stressors. The effect of combined exposure to both work stressors on atrial fibrillation (AF) incidence is not known. The aim of the present study was to examine the separate and combined effect of job strain and effort-reward imbalance at work on atrial fibrillation incidence. Design and method: The study design was a prospective cohort study. A total of 5,926 white-collar workers (3,021 women and 2,905 men) free of cardiovascular disease at baseline were followed for 18 years. Psychosocial stressors at work were measured according to the job strain and ERI models using validated instruments. AF events were identified in medical databases with universal coverage. Hazard ratios (HR) with 95% confidence intervals were estimated using Cox regression models, adjusting for socioeconomic characteristics, lifestyle-related factors and clinical factors. Results: During the follow-up, 186 AF events were identified. Workers exposed to job strain (HR = 1.8; 95% CI 1.1-2.9) or effort-reward imbalance at work (HR = 1.4; 95% CI 1.1-2.0) had a higher risk of AF, following adjustment for socioeconomic characteristics, lifestyle and clinical risk factors. Combined exposure to job strain and ERI was associated with a two-fold AF risk increase (HR = 2.0; 95% CI 1.3-3.1) in fully adjusted models. Conclusions: Workers exposed to job strain or effort-reward imbalance at work, separately and in combination, were at increased AF risk. Prevention strategies targeting psychosocial stressors at work should be considered to decrease the burden associated with AF.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".