Lifestyle risk factors for chronic disease across family origin among adults in multiethnic, low-income, urban neighborhoods.
Bibliographic record
Abstract
OBJECTIVES: To describe the prevalence and co-occurrence of lifestyle risk factors for chronic disease by family origin. DESIGN: Cross-sectional analysis. SETTING: Multiethnic, low-income, urban neighborhoods in Montreal, Canada. PARTICIPANTS: 2033 adults (42.2% male), mean age 39.7 (standard deviation 6.4) years OUTCOME MEASURES: Smoking, level of physical activity, dietary habits, body mass index. METHODS: Subjects completed self-report questionnaires on sociodemographic characteristics, height, weight, and lifestyle behaviors. We tested family origin (based on language first learned in childhood and country of birth) as an independent correlate of co-occurrence (having at least two lifestyle risk factors) in multivariate logistic regression analyses. RESULTS: The prevalence of smoking and poor diet was highest among participants of French Canadian family origin. Although physical inactivity was uniformly high across family origins, it was highest among participants of Portuguese, Italian, and Haitian family origin. Obesity was highest among Europeans. The prevalence of smoking was lowest among Haitians; poor diet was lowest among South Asians; and physical inactivity was lowest among Eastern Europeans. Obesity was lowest among Asians, with the exception that 55.9% of South Asians were overweight or obese. Relative to French Canadians, adults in all other family-origin groups had a lower risk of co-occurrence of lifestyle risk factors. Adults of Asian family origin had the lowest prevalence of co-occurrence of lifestyle risk factors. CONCLUSION: Variation in the distribution of lifestyle risk factors may explain in part differences in chronic disease morbidity and mortality across ethnic groups. Prevention programs should take differential distribution of lifestyle risk factors 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".