Collaboration with First Nations Communities to Produce Tailored Community-Driven Results
Bibliographic record
Abstract
IntroductionWhile First Nations communities are well aware of the unique health challenges and requirements of their populations, research evidence is often needed to support this knowledge. First Nations communities face continual challenges accessing data pertaining to the health of their people that is held by the government or other organizations. Objectives and ApproachThrough the Applied Health Research Question (AHRQ) program at ICES, First Nations communities in Ontario, Canada, have an avenue to access vital population health information about their people. While keeping questions of privacy, data sovereignty, data governance, and the OCAP® principles at the forefront, First Nations partners are active members and collaborators on community driven projects that are of importance to their communities. An Indigenous health data training program has also been developed to run concurrently with these projects, to enhance research knowledge and capacity within partner First Nations communities. ResultsFirst Nations community partners are the main drivers in deciding and refining the research questions for their projects. They are engaged throughout the project process to ensure the production of results that suit the specific needs of the partners. Project results are only shared with the partners, who utilize and disseminate them as appropriate within their communities. Conclusion / ImplicationsWith access to previously difficult to access population health data sources, First Nations communities are able to use health system data as an additional tool to better plan and implement community health programs, to lobby for additional funding, and ultimately to contribute to positive policy change.
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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 | 0.160 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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".