Socioeconomic position, psychosocial work environment and cerebrovascular disease among women: the Finnish public sector study
Bibliographic record
Abstract
BACKGROUND: The excess risk of fatal and non-fatal cerebrovascular disease in people from low socioeconomic positions is only partially explained by conventional cerebrovascular risk factors. This has led to the suggestion that poor psychosocial work environments provide important additional explanatory power. However, little evidence is available for women. METHODS: We examined whether job demands or job control contributed to the socioeconomic gradient in cerebrovascular disease among 48 361 women aged 18-65 years. Job demands, job control and behavioural risk factors were self-reported in 2000-2002; socioeconomic position (as indexed by occupational class) and all of the health measures were obtained from registers. The outcome was recorded hospitalization or death from cerebrovascular disease. RESULTS: During a mean follow-up of 3.4 years, 124 women had a new cerebrovascular disease event. The risk was 2.3 (95% CI 1.3-3.9) times higher among women in low vs high socioeconomic positions. Adjustment for conventional risk factors, such as prevalent hypertension, coronary heart disease, diabetes, smoking, heavy alcohol consumption, physical inactivity and obesity, attenuated this excess risk by 23%. In contrast, adjustment for job demands and job control actually amplified the gradient by 36% suggesting a suppression effect. CONCLUSIONS: In this contemporary cohort of employed women, job demands-alone and in combination with job control-suppressed rather than explained socioeconomic differences in cerebrovascular disease.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".