Cervical and breast cancer screening outcomes among Métis people in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Breast and cervical cancer rates among Métis people in Canada are higher than non-Indigenous people but the impact of cancer screening is uncertain. This study investigated breast and cervical cancer screening participation, retention, and follow-up care among age-eligible Métis people living in Alberta compared to their non-Métis counterparts from 2006 to 2022. METHODS: Data from the Otipemisiwak Métis Government of the Métis Nation within Alberta (MNA) Identification Registry were linked to the Alberta Breast and Cervical Cancer Screening Programs, and the Alberta Cancer Registry for the years 2006 to 2022. Relative and absolute differences in rates, percentages, and means/medians were calculated between Métis and non-Métis people, including age-eligible females, people with a cervix, and people who have taken gender-affirming hormones for five or more years. Trends were assessed using suitable Joinpoint models. RESULTS: Métis and non-Métis people had similar breast and cervical cancer screening participation and retention rates. However, the time between abnormal cancer screening results and follow-up tests was longer for Métis people compared to non-Métis people. Métis people had higher proportions of abnormal cytology test results, and more were diagnosed with advanced-stage cervical cancer (p = 0.06) than non-Métis people. There were no differences in breast cancer stage at diagnosis. CONCLUSION: Ongoing collaboration between the MNA and Screening Programs will build on evidence from the current study to support cancer screening programs and services for all Métis people in Alberta.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".