Métis Peoples and Cancer: A Scoping Review of Literature, Programs, Policies and Educational Material in Canada
Bibliographic record
Abstract
Much of the existing Indigenous cancer research focuses on First Nation populations or reports on pan-Indigenous data that include First Nations, Métis, and Inuit metrics together, which fails to capture the distinct lived realities, experiences of colonialism, and culture of each Indigenous group. The purpose of this scoping review was to summarize existing knowledge on cancer among Métis peoples in Canada, offering direction to researchers, institutions, and policymakers for future actions that enhance Métis-specific cancer surveillance and cancer care. We searched Embase, Medline, iPortal, and Proquest Theses and Dissertations databases, Google Scholar and Google, alongside ten websites relevant to cancer and Métis peoples. Two reviewers gathered 571 records. After screening, 77 records were included. Data show that Métis peoples experience higher behavioral risk factors, lower screening participation, higher cancer incidence for some cancers, and higher mortality rates compared to the non-Indigenous population. Existing research is piece-meal and researchers emphasize that there is inadequate Métis-specific cancer data. There is a need for targeted, Peoples-specific cancer control interventions to reduce these health inequities and a coordinated, Peoples-specific approach to cancer research. These efforts must involve collaboration among Métis Nations and organizations, provincial governments and agencies, researchers, and policymakers.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.037 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".