Incorporating sex and gender considerations in research on psychosocial work exposures and cardiovascular diseases: A systematic review of 55 prospective studies
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality, with disparities observed between males and females. Psychosocial work exposures (PWE), including workload, job control, reward and long working hours, are associated with CVD development. Despite higher prevalence among females, the association with CVD is consistently observed in males, with limited explanations for these differences. This study aimed to examine the consideration of sex and gender in prospective studies within systematic reviews on PWE - specifically, the demand-control model, the effort-reward imbalance model, and long working hours - and CVD. Conducting a systematic review, we assessed sex and gender considerations using criteria from the Sex and Gender Equity in Research (SAGER) guidelines. While most studies recognized potential sex and gender differences in the associations between PWE and CVD, only about half of the 28 studies that included both sexes (15 studies) analyzed females and males separately. Moreover, few studies included criteria for sex- and gender-based analyses. Less than half of the studies (23 studies) incorporated a sex and/or gender perspective to discuss observed differences and similarities between men and women. Although there is a rising trend in integrating sex and gender considerations, significant gaps persist in methodologies and reporting, highlighting the need for comprehensive incorporation of sex and gender considerations to bolster CVD prevention strategies and policies.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".