Abstract IA017: Two-row wampum & Indigenous cancer care services: Building respectful parallels of sovereignty along the cancer care continuum.
Bibliographic record
Abstract
Abstract Quality-improvement (QI) roundtables collected shared voice of Indigenous and non-Indigenous cancer care providers working in and around ancestrally related Native Nations—Canada—USA. Findings were coupled with triangulated, aggregated, and de-identified Centers for Disease Control, Indian Health Services, and New York state information. Collectively, results spoke to the journey of Indigenous communities across the cancer care continuum, translating QI initiatives into community change via Indigenous patient navigation services wrapped in a framework of historical Wampum agreements between Indigenous Nations, Canada, and the United States. Outcomes discuss building cancer care collectives, translational QI methods towards in-person and virtual health care, and innovative grass-roots partnerships towards creative community outreach, engagement, and education. A short video documenting this QI journey was co-created with Indigenous film makers and US-based cancer center media and will be screened. Citation Format: Rodney Haring. Two-row wampum & Indigenous cancer care services: Building respectful parallels of sovereignty along the cancer care continuum. [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr IA017.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.004 |
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".