Supporting health equity for First Nations, Inuit and Métis peoples
Bibliographic record
Abstract
The National Collaborating Centre for Indigenous Health (NCCIH) is unique among the National Collaborating Centres as the only centre focused on the health of a population. In this fifth article of the Canada Communicable Disease Report’s series on the National Collaborating Centres and their contribution to Canada’s public health response to the coronavirus disease 2019 (COVID-19) pandemic, we describe the work of the NCCIH. We begin with a brief overview of the NCCIH’s mandate and priority areas, describing how it works, who it serves and how it has remained flexible and responsive to evolving Indigenous public health needs. Key knowledge translation and exchange activities undertaken by the NCCIH to address COVID-19 misinformation and to support the timely use of Indigenous-informed evidence and knowledge in public health decision-making during the pandemic are also discussed, with a focus on acting on lessons learned moving forward.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 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".