Effort-reward imbalance at work assessed at midlife and prediabetes prevalence assessed 18 years later in a prospective cohort of white-collar workers
Bibliographic record
Abstract
OBJECTIVES: Evidence suggests that workers exposed to psychosocial stressors at work from the effort-reward imbalance (ERI) model are at increased risk for type 2 diabetes mellitus (T2DM). However, evidence about the effect of ERI on prediabetes is scarce. This study aimed to examine the association between effort-reward imbalance at work, glycated hemoglobin level and the prevalence of prediabetes in women and men from a prospective cohort study. METHODS: This study was conducted among 1354 white-collar workers followed for an average of 18 years. Effort-reward imbalance at work was measured in 1999 to 2001 using a validated instrument. Glycated hemoglobin was assessed at follow-up (2015 to 2018). Differences in mean glycated hemoglobin levels were estimated with linear models. Prediabetes prevalence ratios (PRs) were computed using robust Poisson regression models. RESULTS: In women, those exposed to effort-reward imbalance at work at baseline had a higher prevalence of prediabetes (PR = 1.60, 95% confidence interval: 1.02-2.49) at follow-up following adjustment for sociodemographic, lifestyle-related, clinical, and other occupational risk factors. There was no difference in mean glycated hemoglobin levels. CONCLUSION: Among women, effort-reward imbalance at work at midlife was associated with the prevalence of prediabetes, at older age. Preventive workplace interventions aiming to reduce the prevalence of effort-reward imbalance at work may be effective to reduce the prevalence of prediabetes among women.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".