Lifestyle risk factors for chronic disease by family origin among children in multiethnic, low-income, urban neighborhoods.
Bibliographic record
Abstract
OBJECTIVES: To describe the prevalence of lifestyle risk factors (LRF) for chronic disease by family origin (FO) among children in multiethnic, low-income, urban neighborhoods. DESIGN: Cross-sectional analysis. SETTING: 16 elementary schools located in disadvantaged, multiethnic neighborhoods in Montreal, Canada. PARTICIPANTS: 4659 schoolchildren aged 9-12 in grades 4-6. OUTCOME MEASURES: Smoking, level of physical activity, dietary habits, body mass index, sedentary behavior. METHODS: Subjects completed self-report questionnaires on sociodemographic characteristics and lifestyle behaviors; height and weight were measured in a standardized protocol. Fourteen FO groupings were identified based on language(s) spoken and countries of birth of both subjects and parents. We tested FO as an independent correlate of having 2 or more LRF, using the generalized estimating equations method. RESULTS: Relative to Canadian children, a higher proportion of Haitian, Portuguese, and other Central American/Caribbean children had 2 or more LRF, the proportion was similar among Cambodian, Vietnamese, Chinese, South American, East European, Arabic, Italian, and South Asian children, and lower among Salvadorean children. CONCLUSION: Prevention programs for youth should take differential distribution of LRF by ethnicity into account.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".