Educational inequalities in blood pressure and cholesterol screening in nine European countries
Bibliographic record
Abstract
BACKGROUND: To perform the first European overview of educational inequalities in the use of blood pressure and cholesterol screening. METHODS: Data were obtained on the use of screening services according to educational level from nationally representative cross-sectional surveys in Belgium, Czech Republic, Denmark, Estonia, Finland, Hungary, Italy, Latvia and Lithuania. Screening rates were examined in the preceding 12 months and 5 years, for respondents 35+ years (45+ for women). ORs comparing low- to high-educated respondents were estimated using logistic regression controlling for age. RESULTS: Inequalities in cholesterol screening favouring higher socioeconomic groups were demonstrated with statistical significance among men in four countries, whereby men with higher education were more likely to receive screening, with 1.22 as the highest OR. Among women, a similar pattern was found. Inequalities in blood pressure screening were even smaller and less often statistically significant. Hungary was the only country with higher rates of both types of screening in the low-educated group. In other countries, pro-high inequalities were slightly increased after controlling for self-rated health. CONCLUSIONS: All European countries in this study had small educational inequalities in the utilisation of blood pressure and cholesterol screening. These inequalities are smaller than those previously observed in the USA. Further comparative studies need to distinguish between screening for preventive purposes and screening for treatment and control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".