Closing the gaps in cancer screening with First Nations, Inuit, and Métis populations: A narrative literature review
Bibliographic record
Abstract
The objective of this review is to identify cancer screening rates amongst First Nations, Inuit, and Metis to inform cancer screening practices by identifying facilitators and barriers from interventions specific to Indigenous peoples. The Canadian Partnership Against Cancer along with First Nation, Inuit, Metis stakeholders recognise the need to improve cancer screening rates among the Indigenous peoples of Canada (Beben & Muirhead, 2016). And, together, developed the First Nations, Inuit and Metis Action Plan on Cancer Control which included four strategic areas of focus: Community-based health human resource skills and capacity, and community awareness Culturally responsive resources and services Access to programs and services in remote and rural communities Patient identification systems (Canadian Partnership Against Cancer, 2011). This narrative literature review identifies several areas in information management and cancer screening that need attention to effectively improve cancer screening participation rates and associated health outcomes in First Nation, Inuit, and Metis populations. Cancer screening program development needs to be inclusive of those receiving the screening; barriers and facilitators to screening are cancer-specific and provide valuable information for improving cancer screening. Information is available to markedly improve cancer screening uptake within First Nation, Inuit, and Metis people.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".