Risk factors and chronic conditions among Aboriginal and non-Aboriginal populations.
Bibliographic record
Abstract
BACKGROUND: In Canada, the prevalence of behavioural risk factors and chronic conditions differs for Aboriginal and non-Aboriginal populations, but little research has examined changes over time. This study compares several major risk factors and chronic conditions in Aboriginal and non-Aboriginal populations not living on reserves in the North (Yukon, Northwest Territories, Nunavut) and in southern Canada at two time points. DATA AND METHODS: The data are from cycle 1.1 (2000/2001) and cycle 3.1 (2005/2006) of the Canadian Community Health Survey: 115,990 respondents aged 20 or older, and 118,716 respondents, respectively. Overall, 3.8% of respondents reported Aboriginal cultural or racial background. Crude prevalence estimates, adjusted odds ratios, and bootstrap-derived confidence intervals were calculated for seven risk factors and nine chronic conditions at each time point. RESULTS: In 2000/2001, Aboriginal people in the North were more likely than those in southern Canada to be obese, smoke daily and have infrequent physical activity, but less likely report a number of chronic conditions. Between 2000/2001 and 2005/2006, the odds of reporting risk factors increased among Aboriginal people in the North, and differences in the prevalence of chronic diseases were less pronounced. Few differences between non-Aboriginal respondents in the North and in southern Canada were observed. INTERPRETATION: Compared with southern Canada, the profile of health is changing more rapidly for Aboriginal than non-Aboriginal populations in the North, and appears to be worsening for the former.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".