How well does social variation mirror secular change in prevalence of cardiovascular risk factors in a country in transition?
Bibliographic record
Abstract
OBJECTIVES: The social and cultural transition among the Inuit in Greenland over the last generations has in ecological studies been linked to changes in cardiovascular risk factors. To permit analyses at the individual level, we propose a categorization of participants in a cross-sectional study according to their relative position in the process of social change. METHODS: Data was included from two cross-sectional population surveys in 1993-1994 (N = 1,580) and 2005-2009 (N = 2,834). Socioeconomic factors, mental health, health behavior, obesity, blood lipids, blood pressure, and prevalence of diabetes were compared between the surveys and among groups at various degree of social change defined from current residence, job, and education. General linear models and logistic regression analysis were applied. RESULTS: Most outcome variables showed statistically significant difference between the two studies indicating secular change, and for most the gradient in the ranked social groups was in agreement with the observed secular change. This included housing conditions, wealth, diet, smoking, alcohol, physical activity, obesity, and for women also non-HDL cholesterol and hypertension. Anxiety and depression increased over time but decreased with social group for women. Prevalence of type 2 diabetes has increased, but we found no differences among social groups. Serum triglyceride and for men non-HDL cholesterol and hypertension showed inconsistent results. CONCLUSIONS: For a majority of the examined cardiovascular risk factors, social population groups defined from cross-sectional data adequately mirror secular change.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".