Effects of neighbourhood income on reported body mass index: an eight year longitudinal study of Canadian children
Bibliographic record
Abstract
BACKGROUND: This study investigates the effects of neighbourhood income on children's Body Mass Index (BMI) from childhood (ages 2-3) to early adolescence (ages 10-11) using longitudinal data. METHODS: Five cycles of data from the Canadian National Longitudinal Survey of Children and Youth are analyzed for a sub-sample of children (n = 2152) aged 2-3 at baseline (1994) and assessed at two year intervals to 2002. Body mass index percentiles are based on height/weight estimates reported by proxy respondents (child's person most knowledgeable). Family and neighbourhood factors were assessed at baseline. The prevalence of neighbourhood low income was obtained from the 1996 Census and divided into three categories from 'most poor' to 'least poor'. Longitudinal modelling techniques were applied to the data. RESULTS: After controlling for individual/family factors (age, sex, income, education, family structure) living in the 'most poor' neighbourhood was associated with increasing BMI percentile (1.46, 95% CI 0.16 to 2.75) over time compared to a 'middle' income neighbourhood. Living in an urban (vs. rural) neighbourhood was associated with a decreased BMI percentile (-3.57, 95% CI -6.38 to -0.76) across all time periods. CONCLUSION: These findings provide evidence that effects of neighbourhood disadvantage on children's BMI occur between childhood and early adolescence and suggest that policies should target the conditions of childhood, including the neighbourhood environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".