Assessing Breast Cancer Screening and Outcomes Among First Nations Women in Alberta
Bibliographic record
Abstract
Background Breast cancer (BC) incidence rates for First Nations (FN) women in Canada have been steadily increasing and are often diagnosed at a later stage. Despite efforts to expand the reach of BC screening programs for FN populations in Alberta (AB), gaps in screening and outcomes exist. Methods Existing population-based administrative databases including the AB BC Screening Program, the AB Cancer Registry, and an AB-specific FN registry data were linked to evaluate BC screening participation, detection, and timeliness of outcomes in this retrospective study. Tests of proportions and trends compared the findings between FN and non-FN women, aged 50–74 years, beginning in 2008. Incorporation of FN principles of ownership, control, access, and possession (OCAP ® ) managed respectful sharing and utilization of FN data and findings. Results The average age-standardized participation (2013-8) and retention rates (2015-6) for FN women compared to non-FN women in AB were 23.8% ( P < .0001) and 10.3% ( P = .059) lower per year, respectively. FN women were diagnosed with an invasive cancer more often in Stage II ( P-value = .02). Following 90% completion of diagnostic assessments, it took 2–4 weeks longer for FN women to receive their first diagnosis as well as definitive diagnoses than non-FN women. Conclusion Collectively, these findings suggest that access to and provision of screening services for FN women may not be equitable and may contribute to higher BC incidence and mortality rates. Collaborations between FN groups and screening programs are needed to eliminate these inequities to prevent more cancers in FN women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".