Rural-urban differences in overweight and obesity among a large sample of adolescents in Ontario
Bibliographic record
Abstract
OBJECTIVE: Increasing our understanding of the differences between obesity and overweight status across various geographical areas may have important public health implications. We aimed to explore prevalence and factors (i.e., demographic and lifestyle) associated with overweight and obesity among youth across urban, suburban and rural settings. METHODS: A cross-sectional study used self-reported data collected from students (grades 9-12) attending 76 high schools in Ontario, Canada, as part of the SHAPES-Ontario study (2005-2006). Of the 34 578 eligible students selected to complete the Physical Activity Module in the 76 participating schools, 73.5% (n=25 416; 50.8% males, 49.2% females) completed the survey. Univariate and multivariate analyses were conducted using body mass index for weight measurement and self-reported data on lifestyle factors, and self-perception of body weight. RESULTS: The overall prevalence of overweight and obesity was 14.3% and 6.3%, respectively. The prevalence of overweight in urban, suburban and rural areas was 14.6%, 13.8% and 15.1%, respectively, while the prevalence of obesity was 6.3%, 6.0% and 6.7%, respectively, and the difference was significant (chi(2)= 16.53, p<0.05). In the multivariate logistic regression analysis, age, TV watching, level of urbanization and perception of body weight were important predictors of overweight and obesity. CONCLUSION: Our understanding of how overweight and obesity rates vary depending on the level of urbanization may help health professionals to either tailor programs to the needs of the individuals living in these different areas or to target existing programs to the contexts where they are most likely to have an impact.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".