The geography of overweight in Quebec : a multilevel perspective
Bibliographic record
Abstract
OBJECTIVES: \nExplore the contextual aspects of overweight in Quebec through multilevel modelling, using a purposely designed set of spatial units and a few area-based characteristics. \nMETHODS: \nData came from the Canadian Community Health Survey (CCHS Cycle 2.1). Multilevel logistic regressions were performed to test for the presence of an independent contextual effect on overweight and obesity (BMI > or = 25 kg/m2), separately for men and women. Modelling considered individual attributes, including some lifestyle aspects, and contextual characteristics. A geographic grid integrating spatial elements related to overweight and obesity in the literature was developed. Also, an area-level residuals analysis was carried out to identify spatial units presenting higher or lower odds of being overweight. \nRESULTS: \nAfter accounting for individual and area-level characteristics, there remain significant geographic variations in overweight in Quebec. Although this contextual effect is small for men and women, many spatial units differ significantly from the provincial average. There are differences between the geography of overweight in men and women which suggest that socio-economic mechanisms and land use patterns underlying overweight might be different between genders. Also, there is considerable variability within rural and urban areas. \nCONCLUSION: \nA complex geography of overweight is revealed. Small-scale studies, as well as methodological and data developments, are needed to deepen our understanding of this geography.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".