Obesity, overweight and ethnicity.
Bibliographic record
Abstract
OBJECTIVES: This article describes the prevalence of self-reported overweight and obesity, based on body mass index (BMI), by ethnicity and examines the influence of time since immigration within and between ethnic groups. DATA SOURCES: Results are based on data from two cycles of Statistics Canada's Canadian Community Health Survey, conducted in 2000/01 and 2003. ANALYTICAL TECHNIQUES: Weighted prevalences of overweight (BMI > or =25) and obesity (BMI > or =30) were calculated by sex and ethnicity for the population aged 20 to 64. Multiple logistic regression models were used to examine associations between overweight/obesity and ethnicity, and within and between ethnic groups based on time since immigration, controlling for age, household income, education and physical activity. MAIN RESULTS: Aboriginal men and women had the highest prevalences of overweight and obesity; East/Southeast Asians, the lowest. Independent of age, household income, education and physical activity, Aboriginal people had elevated odds of overweight and obesity, compared with Whites; South Asians and East/Southeast Asians had significantly lower odds. Recent immigrants (10 years or less) had significantly lower prevalences of overweight, compared with non-immigrants, but this difference tended to disappear over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".