Abstract IA-37: Addressing cancer health disparities among Indigenous communities
Bibliographic record
Abstract
Abstract Many Indigenous Peoples around the world continue to experience substantial inequities in health as a result of the enduring legacy of colonisation, marginalization and disempowerment. While there are many thousands of miles that separate our presenters by distance, we are united in our conviction to work collaboratively to improve the health and wellbeing of Indigenous peoples and to honor the cultural diversity and strengths within our communities with whom we are fortunate to work alongside. Providing global, national and regional perspectives from New Zealand, Australia and Canada, this presentation will capture both the common strengths and shared challenges faced by Indigenous Peoples within the realm of health, wellness, and cancer. Harnessing learnings and reflections from previous World Indigenous Cancer Conferences (WICCs), and the presenters research activities, they will discuss the challenges and solutions to improve cancer surveillance, strengthening our ability to develop and monitor cancer control plans with a focus on improving equity in cancer outcomes. This session directly responds to the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) and Indigenous People's rights to self-determination. Citation Format: Nadine R. Caron, Gail Garvey, Nina Scott, Warren Clarmont, Kevin Linn. Addressing cancer health disparities among Indigenous communities [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr IA-37.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.009 |
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".