Cancer risk factors and screening in First Nations in Ontario
Bibliographic record
Abstract
INTRODUCTION: A lack of identifiers in health administrative databases limits our understanding of the cancer burden in First Nations. This study compares cancer risk factors and screening between First Nations in Ontario (on and off reserve) and non-Aboriginal Ontarians using two unique health surveys. METHODS: We measured age-standardized prevalence estimates using the First Nations Regional Health Survey (RHS) Phase 2, 2008/10 (for First Nations on reserve) and the Canadian Community Health Survey (CCHS), 2007-2013 (for First Nations off reserve and non-Aboriginal Ontarians). We used prevalence rate ratios (RR) and Pearson's chisquare tests for differences in proportions to compare estimates between First Nations (on and off reserve) and non-Aboriginal Ontarians. RESULTS: A higher proportion of First Nation men, women and adolescents on reserve smoked (RR = 1.97, 2.78 and 7.21 respectively) and were obese (RR = 1.73, 2.33 and 3.29 respectively) compared to their non-Aboriginal counterparts. Similar patterns were observed for First Nations off reserve. Frequent binge drinking was also more prevalent among First Nation men and women living on reserve (RR = 1.28 and 2.22, respectively) and off reserve (RR = 1.70 and 1.45, respectively) than non-Aboriginal Ontarians. First Nation men and women on reserve were about half as likely to consume fruit at least twice per day and vegetables at least twice per day compared to non-Aboriginal men and women (RR = 0.53 and 0.54, respectively). Pap test uptake was similar across all groups, while First Nation women on reserve were less likely to have had a mammogram in the last five years than non-Aboriginal women (RR = 0.85). CONCLUSION: First Nations, especially those living on reserve, have an increased risk for cancer and other chronic diseases compared to non-Aboriginal Ontarians. These results provide evidence to support policies and programs to reduce the future burden of cancer and other chronic diseases in First Nations in Ontario.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".