Cancer Screening Interventions in Indigenous Populations: A Rapid Review
Bibliographic record
Abstract
Cancer screening is an important component of a cancer control strategy. Indigenous people in Canada have higher incidence rates for many types of cancer, including those that can be detected early or prevented through organized screening programs. Increased participation and retention in cancer screening is critical to improved population health outcomes amongst Indigenous people. This rapid review evaluates cancer screening interventions published in the last six years. Included studies demonstrated increased participation in breast, colorectal, or cervical cancer screening programs in Indigenous populations or showed promise of increased participation based on the factors that influence people's screening practices, such as knowledge, attitude, or intent to screen. The Preferred Reporting Items for Systematic Reviews guided the search strategy. The review identified 85 articles with 12 meeting the specified criteria: seven studies reported an increase in cancer screening participation and five studies reported improved knowledge, attitude, or intent to screen. The use of multiple culturally appropriate strategies in co-designed studies were the most effective. This review will be used to inform First Nations (FN) populations and Screening Programs in Alberta of potential strategies to address disparities identified through a recent data analysis comparing cancer screening and outcomes between FN and non-FN 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".