Lessons from First Nations partnerships in hepatitis C research and the co-creation of knowledge
Bibliographic record
Abstract
BACKGROUND: Administrative health data provide a rich and powerful tool for health services research. Partnership between researchers and the Ontario First Nations HIV/AIDS Education Circle (OFNHAEC) allowed for comprehensive analyses of the health and economic impacts of hepatitis C virus (HCV) infection in First Nations populations across Ontario, using administrative data. Examples of meaningful involvement of First Nations partners in research using secondary data sources demonstrate how community-based participatory research principles can be adapted to empower First Nations stakeholders and decision-makers. The aim of this review is to summarize and reflect on lessons learned in producing meaningful and actionable First Nations HCV research using health administrative data, from the perspective of health services researchers who collaborated for the first time with First Nations partners. METHODS: We discuss how our relationship with OFNHAEC formed and how engagement contextualized findings and provided opportunities for fostering trust and mutual capacity building. Methods included adherence to data governance principles, agreements outlining ethical conduct, and establishing commitment between partners. RESULTS: Engagement with OFNHAEC enhanced cultural understandings in study conception, design, and analysis, and enabled meaningful lessons for both parties through contextualizing findings together. Partnership ensured attention to factors, such as strength-based approaches and limitations of administrative data in their representation of First Nations peoples, that are not considered in standard HCV health services research using administrative health data. CONCLUSIONS: Collaboration throughout the HCV research provided first-hand experience of the relevance, representation, and importance of incorporating First Nations perspectives in health services research using administrative data.
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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 | 0.270 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.023 | 0.072 |
| Scholarly communication | 0.036 | 0.044 |
| Open science | 0.005 | 0.055 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".