Indigenous access to clinical services along the lung cancer treatment pathway: a review of current evidence
Bibliographic record
Abstract
BACKGROUND: Lung cancer is a deadly cancer. Early diagnosis and access to timely treatment are essential to maximizing the likelihood of survival. Indigenous peoples experience enduring disparities in lung cancer survival, and disparities in access to and through lung cancer services is one of the important drivers of these disparities. In this manuscript, we aimed to examine the current evidence on disparities in Indigenous access to services along the lung cancer treatment pathway. METHODS: A narrative literature review was conducted for all manuscripts and reports published up until July 20, 2022, using Medline, Scopus, Embase, and Web of Science. Following the identification of eligible literature, full-text versions were scanned for relevance for inclusion in this review, and relevant information was extracted. After scanning 1,459 documents for inclusion, our final review included 36 manuscripts and reports that included information on lung cancer service access for Indigenous peoples relative to non-Indigenous peoples. These documents included data from Aotearoa New Zealand, Australia, Canada, and the USA (including Hawai'i). RESULTS: Our review found evidence of disparities in access to, and the journey through, lung cancer care for Indigenous peoples. Disparities were most obvious in access to early detection and surgery, with inconsistent evidence regarding other components of the pathway. CONCLUSION: These observations are made amid relatively scant data in a global sense, highlighting the need for improved data collection and monitoring of cancer care and outcomes for Indigenous peoples worldwide. Access to early detection and guideline-concordant treatment are essential to addressing enduring disparities in cancer survival experienced by Indigenous peoples globally.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".