Strategies to address barriers and improve bowel cancer screening participation in Indigenous populations, particularly in rural and remote communities: A scoping review
Bibliographic record
Abstract
BACKGROUND: Australian populations, particularly in rural and remote communities. There is growing evidence of strategies to increase screening rates amongst Indigenous Australians, however, there are limited strategies specific to rural and remote communities. OBJECTIVE: This review aims to identify strategies that may increase bowel cancer screening rates amongst Indigenous populations, particularly in rural and remote communities. METHODOLOGY: A literature search was undertaken which included peer-reviewed qualitative and quantitative articles of any study design, and grey literature. Evidence from New Zealand, Canada, United Kingdom, and Australia were included, and descriptive numerical and thematic analyses were conducted. The identified strategies were categorised using the National Cancer Policy Board's organisational framework. RESULTS: Nineteen strategies were identified from 23 included articles. The most frequently used strategies were recommendation from a general practitioner, culturally appropriate education resources, and nonresponder follow up. Four strategies were specific to rural and remote communities including alternative distribution of kits and mobile screening. Thirteen strategies aim to address the Knowledge category of the framework, four address Attitudes, four address Ability, and six address Reinforcement. So What?: Several strategies are available to increase bowel cancer screening in Indigenous populations, with very few strategies specifically relating to rural and remote communities. Multiple strategies may maximise the likelihood of participation in screening amongst Indigenous Australians. Implementation may require system-level and local-level changes.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".