Lung cancer in First Nations, Inuit, and Métis peoples in Canada – a scoping review
Bibliographic record
Abstract
Lung cancer is one of the most commonly diagnosed cancers in Canada and a leading cause of cancer mortality. Lung cancer also affects First Nations, Inuit and Métis peoples significantly in Canada, which deserves further investigation as there is a literature gap on this topic. We sought to develop a deeper understanding of lung cancer diagnosis, incidence, mortality, and survival in First Nations, Inuit, and Métis peoples in Canada. A systematic search was conducted in bibliographic databases to identify relevant studies published between January 2000 and March 2023. Articles were screened and assessed for relevance using the Population/ Concept/ Context (PCC) framework. A total of 22 articles were included in the final analysis, of which 13 were Inuit-specific, 7 were First Nations-specific, and 2 were Métis-specific. The literature suggests that comparative incidence, mortality, and relative risk of lung cancer is higher and survival is poorer in First Nations, Inuit and Métis peoples. Lung cancer also has varying impact on these population depending on sex, age, location and other factors. This review illustrates that more comprehensive quantitative and qualitative lung cancer research is essential to further identify the structural causes for the high incidence of the disease.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".