What does meaningful Indigenous community engagement look like? Examining collaboration processes in health systems transformation in southeastern Ontario (Canada)
Bibliographic record
Abstract
Background: The healthcare system in Ontario, Canada is being reorganized into Ontario Health Teams, which are learning health systems based on the principles of integrated care and guided by the Quintuple Aims. Community engagement to address inequities faced by Indigenous peoples is a stated objective of the new Ontario Health Teams. This research aims to examine the collaboration processes with Indigenous community partners in the governance and activities of the Frontenac, Lennox & Addington Ontario Health Team (FLA OHT), located in southeastern Ontario. Methods: Case study method involving community-based participatory research and using principles of Ownership, Control, Access, Possession (OCAP) in ethical Indigenous health research. The research team includes academic researchers from Queen’s University, FLA OHT leaders and administrators, and Indigenous community members from partner organizations. Data sources include: focus groups and interviews with previous and current Indigenous members of FLA OHT working groups, tables and support structures; focus groups with FLA OHT leadership and administration; and, organizational documents. The final product is a framework to operationalize meaningful collaboration between Indigenous partners and mainstream health systems. Results: Preliminary results from focus groups and interviews with Indigenous members point to four major themes: the importance of relationship-building, drawing and building on previous work, creating and enhancing Indigenous spaces, and increasing Indigenous representation. Barriers to meaningful collaboration include: feeling dismissed and ignored, tokenism, slow-moving work with unclear goals, burdening the Indigenous community, and a lack of accountability and transparency. Enablers include: moving from continual consultation to concrete action, honouring Indigenous peoples’ time with honorariums, having open minds, and ensuring Indigenous peoples feel heard, respected and supported. Conclusions: The importance of action-oriented engagement, adequate funding and resources, and responding actively to Indigenous voices are key steps to ensure that collaboration feels meaningful to Indigenous partners. The framework from this case study may be broadly useful to guide partnership processes between mainstream institutions and Indigenous groups in health systems and policy.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".