Updating the Canadian Obesity Maps: an epidemic in progress
Bibliographic record
Abstract
Obesity is a growing problem in Canada and worldwide. While obesity maps that convey changing rates over time and geography provide a useful way to convey such information, regional obesity surveillance maps for Canada have not been published since 1998. This research provides a summary of changing Canadian obesity rates since that time. METHODS: We computed estimated obesity rates for provinces and territories across Canada from 2000 to 2011. Data were based on Canadian Community Health Survey and corrected for self-report bias. Data reporting the estimated percent of the adult population who are obese were mapped over time overall and by sex according to Canadian province and territory. RESULTS: The data indicate that the estimated prevalence of obesity across Canada has continued to increase over the past 11 years. Current rates exceed 30% in the Maritime provinces (Newfoundland, New Brunswick, Nova Scotia, Prince Edward Island) and in two territories (Northwest Territory, Nunavut). Data for men and women are generally consistent. The major increase in obesity appears to have occurred in the first part of this period, with relatively stable rates found from 2008 to 2011. However, obesity rates are still climbing, warranting continued surveillance efforts. CONCLUSION: Maps showing changing regional obesity rates provide a compelling pan-Canadian portrait that can lead to an impetus for action for the public, health care providers, and decision makers. Such colour-coded maps offer an efficient way to convey complex data that transcends language differences and personalizes the data for the viewer
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.030 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".