Effort-reward imbalance at work, glycated hemoglobin and prediabetes prevalence in a prospective cohort
Bibliographic record
Abstract
Abstract Contex Prospective studies and meta-analyses suggest that psychosocial stressors at work from the effort-reward imbalance model are associated with an increased risk of type 2 diabetes mellitus (T2DM). Prediabetes is an intermediate disorder on the glucose metabolism continuum. It increases the risk of developing T2DM, while also being separately associated with increased mortality. Evidence about the effect of effort-reward imbalance at work on prediabetes is scarce. Objective The objective was to evaluate, in women and men, the association between effort-reward imbalance at work, glycated hemoglobin (HbA1c) concentration and the prevalence of prediabetes in a prospective cohort study. Methods This study was conducted among 1,354 white-collar workers followed for an average of 16 years. Effort-reward imbalance at work (ERI) was measured at baseline (1999-2001) using a validated instrument. HbA1c was assessed at follow-up (2015-18). Several covariates were considered including sociodemographics, anthropometric, and lifestyle risk factors. Differences in mean HbA1c concentration were estimated with linear models. Prediabetes prevalence ratios (PRs) were computed using Poisson regressions models. Results In women, those exposed to effort-reward imbalance at work had a higher prevalence of prediabetes (adjusted PR=1.52, 95% confidence interval: 1.01-2.29). There was no difference in HbA1c concentration among those exposed and those unexposed to an effort-reward imbalance at work. Conclusion Among women, effort-reward imbalance at work was associated with the prevalence of prediabetes. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".