Association entre les caractéristiques de l'environnement physique des quartiers résidentiels et les trajectoires de z-scores d'IMC chez les adolescents à Montréal : étude de cohorte NDIT
Bibliographic record
Abstract
Context – The obesity epidemic is a concern, especially in Canada. Although many studies have examined the relationship between obesity and the residential environement, few studies have investigated this association taking into account the environment as a whole during adolescence. The objective is to study the association between the characteristics of the neighborhood’s physical environment and the trajectories of body mass index z-score (BMIz) in adolescents in Montreal. Methods – Data were from the NDIT cohort study of 1293 students from Montreal. BMI, expressed as z-score, was calculated using the CDC norms from three repeated measures of anthropometric data (on average at 12, 15 and 17 years old) performed in a standardized manner. The residential environment variables were retrieved from a geographic information system. A hierarchical cluster analysis was conducted to identify distinct neighborhood types from five environmental variables. Then, multivariate analyzes stratified on sex were conducted using a linear mixed regression. These models were adjusted for the country of birth of the participants, the education of their parents, and the percentage of people below the low-income threshold in the neighborhood. Results – This study involved 378 girls and 331 boys. Three types of neighborhoods have been identified. The “countryside” areas have a lot of vegetation, very few services and a low population density. The “urban” cluster includes areas with a high population density, many services and little vegetation. Finally, the “village-urban” cluster is distinguished from the countryside by its strong land use diversity and more walkable areas. After adjusting for potential confounders, no association between neighborhoods and BMIz over time was found in boys. For girls, living in an “urban” area seemed to decrease their IMCz over time whereas it was the opposite for "urban-village" area. Conclusion – Policies preventing obesity should recommend environments that promote walking as well as the availability of services. The study of all neighborhood characteristics appears to be the preferred approach in this type of research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".