Greater prevalence of select chronic conditions among Aboriginal and South Asian participants from an ethnically diverse convenience sample of British Columbians
Bibliographic record
Abstract
Canadians currently experience elevated rates of chronic conditions compared with past populations, and ethnic differences in the experience of select chronic conditions have previously been identified. This investigation examined the prevalence of select chronic conditions among an ethnically diverse convenience sample of British Columbian adults. A sample of adults (≥18 years) from around the province of British Columbia, including Aboriginal (n = 991), European (n = 3650), East Asian (n = 466), and South Asian (n = 228), were evaluated. Individuals reported their personal histories of cardiovascular disease and diabetes, and physical activity behaviour. Direct measures of health status included body mass index, waist circumference, resting blood pressure, and nonfasting blood glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, and glycosylated hemoglobin A1C. All ethnic groups were found to have high rates of low HDL (>33%), physical inactivity (>31%), hypertension (>16%), and ethnic-specifically defined obesity (>23%) and abdominal obesity (>33%). Aboriginal and South Asian populations generally demonstrated higher rates of select chronic conditions. The implementation of ethnic-specific body composition recommendations further underscores this poorer health status among South Asian populations. Actions to improve chronic condition rates should be undertaken among all ethnic groups, with particular attention to Aboriginal and South Asian populations.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| 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".