Creating a Canadian Indigenous Research Network Against Cancer to Address Indigenous Cancer Disparities
Bibliographic record
Abstract
PURPOSE: In Canada, indigenous peoples' cancer rates have increased, but cancer screening rates tend to be lower. When coupled with poor cancer prognosis, treatment barriers, and inaccessible health care, indigenous patients with cancer experience many unmet needs. Further complicating their journey is a multijurisdictional system that complicates cancer control services, treatments, patient supports, and cancer surveillance. To address these issues, the Canadian Indigenous Research Network Against Cancer (CIRNAC) was developed. This article describes the forerunners and consultative process that created the network and the consensus model developed to ground this network with, by, and for indigenous peoples. METHODS: A consultative workshop was held to (1) establish and increase network membership, (2) enhance partnerships with indigenous communities and other researchers, and (3) develop an indigenous-led research program, new funding, and related initiatives. RESULTS: Participants viewed the CIRNAC as a reflective parallel network led by indigenous peoples that would identify research priorities within Canada, assess how these priorities align with indigenous patients' cancer care and research needs, and cross-check to see if these priorities align with each other. The network would also advocate for indigenous elders/knowledge holders and community grassroot processes to drive research and training, thus demonstrating the power of the community voice and lived experience in research. In addition, the network would foster research partnerships to investigate alternative indigenous models for cancer prevention, care, treatment, and support. CONCLUSION: The CIRNAC evolved as a viable vehicle to address cancer with, for, and by indigenous peoples. The network is guided by a preamble, a set of aims, and an inclusion engagement circle model. It is evolving through major world initiatives, with the aim of formally becoming an internationally linked national network.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".