Assessing colorectal cancer screening and outcomes among First Nations people in Alberta
Bibliographic record
Abstract
Abstract Background First Nations (FN) people in Canada are commonly diagnosed with colorectal cancers. Although Canada has treaty responsibilities to ensure FNs people have equitable access to quality health services, access to colorectal cancer screening in Canada by FNs people has not been fully assessed. Methods The objectives of our retrospective population-level study that linked multiple administrative databases were to investigate differences in colorectal cancer screening rates: participation, retention, positivity, follow-up colonoscopy, and invasive colorectal cancer detection, as well as wait times to follow-up colonoscopy, and stages at diagnosis between FNs and non-FNs people in Alberta. All Alberta residents eligible for colorectal cancer screening (aged 50-74) between 2012 and 2018 were included. The study and descriptive methods adhered to FNs principles of ownership, control, access, and possession. Results FNs people were less likely to participate in colorectal cancer screening (lower by 15.9% among women, P < 0.00001, and 17.0% among men; P = 0.0007), and less likely to be retained in the screening program (lower by 11.6%, P = 0.0013, among women and 9.9% %, P = 0.034, among men). They were more likely to screen positive (average difference of 7.0% among women and 7.3%, among men, both P < 0.0002). Invasive colorectal cancer detection rates were higher (3.2/1000) versus (2.3/1000) as were late-stage diagnoses (61% versus 43%, P = P = 0.004) among FNs people than non-FNs people, respectively. Conclusions Higher invasive colorectal cancer detection rates and more late-stage diagnoses in FNs people can be due to lower participation and retention in colorectal cancer screening programs. Understanding and addressing the reasons for these inequities are needed to improve these outcomes for FNs 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".