Cancer Survival in First Nation and Métis Adults in Canada: Follow-up of the 1991 Census Mortality Cohort
Bibliographic record
Abstract
Internationally, Indigenous persons tend to have poorer cancer survival than their non-Indigenous peers. Measuring cancer survival among Indigenous populations presents a particular set of challenges owing largely to their small share of the population and a lack of reliable and valid ethnic identifiers in cancer registries and vital statistics registries. The objectives of this thesis were to explore these challenges and how they are or are not overcome internationally, to produce the first Canada-wide estimates of cancer survival for First Nations and Métis, and to estimate the extent to which different methods of measuring cancer survival yield different results in this application. The systematic review conducted to achieve the first objective revealed that the majority of studies of this topic internationally use a cause-specific approach to measuring survival and that despite common threats to validity posed by information biases, rarely discussed these risks or their potential consequences. Data from the 1991 Census Mortality Cohort, a linkage of the 1991 Long Form Census to the Canadian Cancer Registry and the Canadian Mortality Database through to 2009, were used to accomplish the second and third objectives. Compared to non-Aboriginal cohort members, First Nation and Métis people had poorer survival for nearly all of the most common cancers and the disparity remained after taking differences in income and rurality into account. The differences in results yielded by common methods for survival analysis were shown to vary depending on the population, the type of cancer and whether survival itself or survival disparities were being measured. Taken together, this work advances our knowledge of cancer disparities between First Nation, Métis and non-Aboriginal people in Canada and our understanding of how the data and methods we use impact the magnitude of disparities we measure.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".