An exploration of socioeconomic variation in lifestyle factors and adiposity in the Ontario Food Survey through structural equation modeling
Bibliographic record
Abstract
BACKGROUND: Socioeconomic indicators have been inversely associated with overweight and obesity, with stronger associations observed among women. The objective of the present secondary analysis was to examine the relationships among socioeconomic measures and adiposity for men and women participating in the Ontario Food Survey (OFS), and to explore lifestyle factors as potential mediators of these associations. METHODS: The cross-sectional 1997/98 OFS collected anthropometric measurements, a food frequency questionnaire, data on socio-demographics (age, sex, income, and education) and physical activity from 620 women and 467 men, ages 18 to 75. Based on the 2003 Health Canada guidelines, waist circumference and BMI values were used to derive least risk, increased risk, and high risk adiposity groups. Structural equation modeling was conducted to examine increased risk and high risk adiposity in relation to education and income, with leisure time physical activity, fruit and vegetable intake, and smoking status included as potential mediators of these associations. RESULTS: The probability of high risk adiposity was directly associated with education (beta-0.19, p < 0.05) and income (beta-0.22, p < 0.05) for women, but not for men. Fruit and vegetable intake was a marginally significant mediator of the relationship between education and high risk adiposity for women. Increased risk adiposity was not associated with income or education for men or women. CONCLUSION: The socioeconomic context of adiposity continues to differ greatly between men and women. For women only in the OFS, fruit and vegetable intake contributed to the inverse association between education and high risk adiposity; however, additional explanatory factors are yet to be determined.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".