Effort-Reward Imbalance at Work and the Prevalence of Unsuccessfully Treated Hypertension Among White-Collar Workers
Bibliographic record
Abstract
We examined the association between effort-reward imbalance (ERI) exposure at work and unsuccessfully treated hypertension among white-collar workers from a large cohort in Quebec City, Canada. The study used a repeated cross-sectional design involving 3 waves of data collection (2000-2009). The study sample was composed of 474 workers treated for hypertension, accounting for 739 observations. At each observation, ERI was measured using validated scales, and ambulatory blood pressure (BP) was measured every 15 minutes during the working day. Unsuccessfully treated hypertension was defined as daytime ambulatory BP of at least 135/85 mm Hg and was further divided into masked and sustained hypertension. Adjusted prevalence ratios and 95% confidence intervals were estimated. Participants in the highest tertile of ERI exposure had a higher prevalence of unsuccessfully treated hypertension (prevalence ratio = 1.45, 95% confidence interval: 1.16, 1.81) after adjustment for gender, age, education, family history of cardiovascular diseases, body mass index, diabetes, smoking, sedentary behaviors, and alcohol intake. The present study supports the effect of adverse psychosocial work factors from the ERI model on BP control in treated workers. Reducing these frequent exposures at work might lead to substantial benefits on BP control at the population level.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".