Cancer-Related Risk Factor Prevalence and Screening Participation in Ontario Off-Reserve First Nations and Métis Adults.
Bibliographic record
Abstract
INTRODUCTION: Increasing evidence surrounding the rising burden of cancer among Canadian Aboriginal subpopulations suggests the important need for current data on cancer risk factor prevalence and screening uptake in this population. Several population-based surveys have included Aboriginal respondents over the years, however their use for studying Aboriginal health specifically, has proved challenging. Despite the small sample of Aboriginal respondents in national population-based surveys, the extensive array of health-related indicators within the Canadian Community Health Survey (CCHS) provides the most comprehensive look at cancer-related risk factors and screening behaviour among Aboriginal subpopulations. METHODS: CCHS surveys from 2007 to 2011 were combined to increase the sample of Ontario's off-reserve First Nations and Métis respondents, in order to estimate the prevalence of smoking, obesity, alcohol consumption, physical activity, diet, and colorectal, breast, and cervical screening uptake. Odds ratios adjusted for socioeconomic factors (SES) were obtained, and non-Aboriginal Ontarians were analyzed for comparison. RESULTS: Significantly higher rates of smoking and obesity were found in both the First Nations and Métis population compared to non-Aboriginal Ontarians. Significantly heavier alcohol consumption was reported among First Nations and Métis males, while inadequate fruit and vegetable consumption was more reported among First Nations. Accounting for SES however, resulted in a no longer significant difference in alcohol consumption and fruit and vegetable intake among Métis males and First Nations males, respectively, compared to non-Aboriginal males. First Nations women were more likely to report having had a colorectal cancer screening test in the past two years than non-Aboriginal women.
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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 | 0.000 |
| Scholarly communication | 0.000 | 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".