Why does the social gradient in health not apply to overweight?
Bibliographic record
Abstract
BACKGROUND: In developed countries, there is a negative association between socioeconomic status (SES) and a variety of health outcomes, known as the social gradient in health. This is contrasted by a weak, absent or even positive gradient for overweight. The objective of this study was to investigate why overweight does not follow the social gradient. DATA AND METHODS: Data from adult respondents to the 2004 Canadian Community Health Survey (cycle 2.2) were used. A series of multivariate models regressing overweight and determinants of overweight on household education and household income were performed, stratified by gender. RESULTS: Except for education among women, negative associations between SES measures and overweight emerged. Respondents from higher household income groups reported more meals away from home, compared with those from lower household income groups. In addition, adults in higher-education households were more likely than those in lower-education households to have quit smoking. INTERPRETATION: Differences in food consumption patterns and smoking cessation between SES groups may have contributed to the lack of a clear negative association between household education and income and overweight in the CCHS.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".