Creation of a Métis-Specific Instrument for Cancer Screening: A Scoping Review of Cancer-Screening Programs and Instruments
Bibliographic record
Abstract
Understanding the barriers to and facilitators of cancer screening programs among Indigenous populations remains limited. In the spirit of mutual respect, this co-led, collaborative project was carried out between the Métis Nation of Alberta and Screening Programs from Alberta Health Services (AHS). This scoping review assessed the cancer screening literature for available questionnaires and then identified themes and suitable questions for a Métis-specific cancer screening questionnaire. Literature searches on cervical, breast, and colorectal cancer screening programs and related concepts were conducted in electronic databases, including the Native Health Database, MEDLINE (Ovid), PsycINFO, PubMed, PubMed Central, CINAHL, MEDLINE (Ebsco), Psychology & Behavioral Sciences Collection, and Web of Science. Grey literature was collected from AHS Insite, Open Archives Initiative repository, American Society of Clinical Oncology, European Society of Medical Oncology, Google, and Google Scholar. 135 articles were screened based on the eligibility criteria with 114 articles selected, including 14 Indigenous-specific ones. Knowledge, attitude, belief, behaviour, barrier, and facilitator themes emerged from the review, but no Métis-specific cancer screening instruments were found. Thus, one was developed using existing cancer screening instruments, with additional questions created by the project team. A survey of the Métis population in Alberta will use this questionnaire and provide data to address the burden of cancer among Métis 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 | 0.061 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.037 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".