Cancer-related health behaviors and health service use among Inuit and other residents of Canada’s north
Bibliographic record
Abstract
Objective – To identify the extent to which differences between Inuit and other residents of Canada’s North in a set of health behaviors and health service use related to cancer incidence and diagnosis can be accounted for by demographic, socio-economic and geographic factors. Study Design – Data on residents aged 21-65 who live in Canada’s North are drawn from the 2000-01 and 2004-05 Canadian Community Health Surveys and the 2001 Aboriginal People’s Survey. Methods – Multivariate Logistic regression analysis is applied to 1) a set of health behaviors including smoking, binge drinking and obesity, and 2) a set of basic health service use measures including consultations with a physician and with any medical professional, Pap smear testing and mammography. Results – Higher smoking and binge drinking rates and lower rates of female cancer screening among Inuit are not accounted for by differences in demographic characteristics, education, location of residence or distance from a hospital. Conclusions – Factors specific to Inuit individuals and communities may be contributing to negative health behaviors associated with increased cancer risk, and to a lower incidence of diagnostic cancer screening. Policy interventions to address these issues may need to be targeted specifically to Inuit Canadians.
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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 | 0.001 |
| 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".