Explained and Unexplained Regional Variation in Canadian Obesity Prevalence
Bibliographic record
Abstract
The objective of our study was to examine sociodemographic and behavioral variables underlying the geographic variation of obesity in Canada. We aimed to quantify the share of regional variation in average BMI attributable to commonly cited determinants of obesity and the remaining share, which is attributable to the idiosyncrasies of the regional environment ("regional effects"). Using data from the Canadian Community Health Survey (CCHS) (2004), ordinary least squares (OLS) regression, and Blinder-Oaxaca decomposition to decompose the difference in mean BMI between regions, we quantify two parts of the difference: a share explained by different levels of the covariates and a share explained by those covariates having different effects on BMI in the different regions, using the Atlantic provinces as the reference group. We observed that some differences (e.g., average BMI for males in Quebec compared to the Atlantic provinces) are mostly explained by the different levels of socio-demographic and behavioral covariates, while others (e.g., average BMI for females in Quebec compared to the Atlantic provinces) are mostly explained by the different effects of the covariates on BMI. In the latter scenario, even if covariates were made to be identical in the different regions, the difference in average BMI would persist. Thus, targeting covariates in different regions through plans like physical activity or nutrition policy, income equalization, or education subsidies will have ambiguous effects for addressing disparate obesity levels, being plausible policy options in some regions but less so in others. Future research and policy would benefit from identifying these region-specific attributes that have local implications for BMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".