Ethnic and Regional Differences in Prevalence and Correlates of Chronic Diseases and Risk Factors in Northern Canada
Bibliographic record
Abstract
IntroductionWe investigated ethnic and geographic variations in major chronic diseases and risk factors in northern Canada, an area that is undergoing rapid changes in its social, cultural, and physical environments.MethodsSelf-report data were obtained from the population-based Canadian Community Health Survey in 2000-2001 and 2005-2006 for Aboriginal and non-Aboriginal respondents from the 3 regions of northern Canada: Yukon, Northwest Territories, and Nunavut. Crude prevalence estimates, adjusted odds ratios (AORs), and confidence intervals were calculated for multiple chronic diseases and risk factors.ResultsThe percentage of Aboriginal respondents who reported having any chronic health condition increased between the 2 cycles of data collection, but did not change for non-Aboriginal respondents. AORs for heart disease, arthritis, and asthma varied by ethnicity or region. AORs for overweight, obesity, daily smoking, regular and binge drinking, and infrequent physical/leisure activity were also substantially different for Aboriginal and non-Aboriginal respondents or among respondents from the 3 northern regions.ConclusionThe changing profile of health in northern Canada suggests a need for action on health policy about the delivery of community-based primary prevention interventions and further research about the determinants of health and health care use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".