Colorectal cancer screening participation in First Nations populations worldwide: a systematic review and data synthesis
Bibliographic record
Abstract
Background: First Nations populations have poorer colorectal cancer (CRC) survival compared to non-First Nations populations. Whilst First Nations populations across the world are distinct, shared experiences of discrimination and oppression contribute to persistent health inequities. CRC screening improves survival, however screening rates in First Nations populations are poorly described. This study seeks to define participation rates in CRC screening in First Nations populations worldwide. Methods: A systematic literature search was conducted of PubMed, Embase, Cochrane Library, CINAHL, MEDLINE, grey literature, national registries and ClinicalTrials.gov. All sources were searched from their inception date to 18 February 2024. Studies were included if they reported CRC screening rates in adult (≥18 years) First Nations populations. We aimed to undertake a meta-analysis if there were sufficient data. Quality of papers were assessed using the Joanna Briggs Institute (JBI) appraisal tool. The study was registered with PROSPERO, CRD42020210181. Findings: The literature search identified 1723 potentially eligible published studies. After review, 57 studies were included, 50 from the United States (US), with the remaining studies from Australia, Aotearoa New Zealand (NZ), Canada, Dominica and Guatemala. Additionally, eleven non-indexed reports from national programs in Australia and NZ were included. There were insufficient data to undertake meta-analysis, therefore a systematic review and narrative synthesis were conducted. CRC screening definitions varied, and included stool-based screening, sigmoidoscopy and colonoscopy. US First Nations screening rates ranged between 4.0 and 79.2%, Australia reported 10.6-35.2%, NZ 18.4-49%, Canada 22.4-53.4%, Guatemala 2.2% and Dominica 4.2%. Fifty-five studies were assessed as moderate or high quality and two as low quality. Interpretation: Our findings suggested that there is wide variation in CRC screening participation rates across First Nations populations. Screening data are lacking in direct comparator groups and longitudinal outcomes. Disaggregation of screening data are required to better understand and address First Nations CRC outcome inequities. Funding: None.
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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 | 0.025 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".