Weeneebayko Area Health Authority-ICES-Laurentian University Collaboration: Working together to support communities with Indigenous Health Research in the James and Hudson Bay Region, in Northeast Ontario, Canada.
Bibliographic record
Abstract
ObjectiveThe James and Hudson Bay Region, consisting of six remote Indigenous communities, have experienced barriers accessing regional health data. To inform local health planning, the Minomathasowin Healthy Living department in the Weeneebayko Area Health Authority (WAHA), ICES and Laurentian University developed a Collaboration, to co-create enhanced Indigenous data stewardship. ApproachThe Collaboration combines expertise in Indigenous knowledge with quantitative and qualitative analyses to develop relevant data intended for public dissemination. Through a community-driven and strength-based approach, local knowledge guides the direction of the research. Indigenous data governance principles are applied, supporting local data ownership, and supplementing local knowledge on population health issues. This ensures the development of research projects that have meaningful impacts. The Collaboration is part of a larger partnership and is continually engaging local Indigenous stakeholders. Protocols ensure research is done in a manner that respects and reflects community well-being and is undertaken in a good way. ResultsThe Collaboration is an ongoing, living initiative and has enabled WAHA to become a local hub for Indigenous stakeholders to obtain health data for their respective communities. It adheres to the importance of following protocols within Indigenous communities, acknowledging qualitative research activities can be undertaken at the community-level. Projects from this Collaboration identify and prioritize the most pressing health issues impacting the Region including mental health and addictions, COVID-19 surveillance, hospitalization trends, and the prevalence of lupus. The success of the Collaboration is demonstrated through increased requests from the Region to WAHA for support on health planning and decision-making. Data access barriers in the Region are being addressed through the combined expertise of the Collaboration and local knowledge. This approach is enhancing Indigenous data stewardship. ConclusionsThe Collaboration advocates for Indigenous-led and -driven research that recognizes the value of combining local knowledge with quantitative and qualitative data analyses to put communities first. The Collaboration supports equitable data access and the development of relevant research projects. This is leading to sustainable, impactful health planning for the Region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".