Lung Cancer Screening Participation Among Indigenous Peoples Worldwide: A Systematic Review of Challenges and Opportunities
Bibliographic record
Abstract
ISSUE ADDRESSED: Lung cancer screening (LCS) is crucial for Indigenous populations due to their higher lung cancer incidence rates and poorer outcomes. Despite efforts to establish LCS programmes, evidence on LCS cost-effectiveness, participation rates, facilitators and barriers for Indigenous peoples remains limited. This systematic review aims to address this gap by reviewing available evidence. METHODS: This systematic review conducted searches for relevant articles in PubMed, Scopus, CINAHL, Google Scholar and references/citations of included articles. RESULTS: Fifteen out of 19 eligible studies were conducted in the USA, three in New Zealand and one in Canada, with 23 715 Indigenous participants in the 15 quantitative studies. New Zealand studies found that LCS is cost-effective for Māori, while the participation rate for American Indian/Alaska Natives (4.7%) was lower than for White Americans (21.7%). Facilitators included positive views of LCS, trust in Indigenous-centred care/providers, trusted invitations, family and community support, transportation or flexible scheduling, culturally competent navigators and detailed health education. Barriers included limited knowledge about LCS/eligibility criteria, fear of the screening process or cancer diagnosis, mistrust or negative experiences in healthcare, cost and time constraints, limited transportation/resources and non-inclusive eligibility criteria. CONCLUSIONS: Further research is needed to understand the LCS among Indigenous peoples. Enhancing LCS participation requires leveraging positive experiences and addressing barriers with culturally tailored education and strategic resource allocation. SO WHAT?: For Australia and similar countries preparing for LCSPs, global evidence highlights the need for adequate resources, integration of Indigenous cultural practices and active involvement of Indigenous communities in programme planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".