Adverse effects of psychosocial work factors on blood pressure: systematic review of studies on demand–control–support and effort–reward imbalance models
Bibliographic record
Abstract
OBJECTIVES: A growing body of research has investigated the adverse effects of psychosocial work factors on blood pressure (BP) elevation. There is now a clear need for an up-to-date, critical synthesis of reliable findings on this topic. This systematic review aimed to evaluate the adverse effects of psychosocial work factors of both the demand-control-support (DCS) and effort-reward imbalance (ERI) models on BP among men and women, according to the methodological quality of the studies. METHODS: To be eligible, studies had to: (i) evaluate at least one psychosocial work factor, (ii) evaluate BP or hypertension, (iii) comprise ≥100 workers, (iv) be written in English or French, and (v) be published in a peer-reviewed journal. RESULT: A total of 74 studies were included. Of these, 64 examined the DCS model, and 12 looked at the ERI model, with 2 studies considering both models. Approximately half the studies observed a significant adverse effect of psychosocial work factors on BP. A more consistent effect was observed, however, among men than women. For job strain, a more consistent effect was also observed in studies of higher methodological quality, ie, studies using a prospective design and ambulatory BP measures. CONCLUSIONS: A more consistent adverse effect of psychosocial work factors was observed among men than women and in studies of higher methodological quality. These findings contribute to the current effort of primary prevention of cardiovascular disease by documenting the psychosocial etiology of elevated BP, a major cardiovascular risk factor.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.015 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".