Effort-Reward Imbalance at Work and Recurrent Coronary Heart Disease Events
Bibliographic record
Abstract
OBJECTIVE: Prospective studies have shown that effort-reward imbalance (ERI) at work is associated with the incidence of a first coronary heart disease (CHD) event. However, it is unknown whether ERI at work increases the risk of recurrent CHD events. The objective of this study was to determine whether ERI at work and its components (effort and reward) increase the risk of recurrent CHD in post-myocardial infarction (post-MI) workers. METHODS: We carried out a prospective cohort study of 669 men and 69 women who returned to work after a first MI. ERI at work was assessed by telephone interview using validated scales of reward and psychological demands. The outcome was a composite of fatal CHD, nonfatal MI, and unstable angina. CHD risk factors were documented in medical files and by interview. The participants were followed up for a mean period of 4.0 years (1998-2005). RESULTS: During the follow-up, 96 CHD events were documented. High ERI and low reward were associated with recurrent CHD (respective adjusted hazard ratios [HRs] = 1.75, 95% confidence interval [CI] = 0.99-3.08, and HR = 1.77, 95% CI = 1.16-2.71). There was a gender interaction showing stronger effects among women (respective adjusted HRs for high ERI and low reward: HR = 3.95, 95% CI = 0.93-16.79, and HR = 9.53, 95% CI = 1.15-78.68). CONCLUSIONS: Post-MI workers holding jobs that involved ERI or low reward had increased risk of recurrent CHD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".