Collaboration with Indigenous communities in an integrated care learning health system in Ontario, Canada: a case study of the Frontenac, Lennox & Addington Ontario Health Team
Bibliographic record
Abstract
Context: Health systems in the province of Ontario in Canada have been restructured into Ontario Health Teams (OHTs). OHTs seek to provide integrated care through improving patient experience, improving care provider experience, improving population health outcomes, reducing costs, and addressing health equity - per the Quintuple Aim framework. The Frontenac, Lennox & Addington (FLA) OHT was created in bringing together numerous clinical and community partners, including local Indigenous health groups. Indigenous peoples face health inequities driven by colonization and systematic racism; improving accessibility, cultural appropriateness, and cultural safety of health systems are essential. To ensure that unique needs of Indigenous peoples are met, there should be a collaborative, participatory approach to planning and executing Indigenous-specific evaluation activities, with Indigenous-specific performance indicators. Objectives: 1) To co-design, implement, and evaluate a governance process for collaboration with Indigenous communities, for Indigenous-focussed evaluation of FLA OHT activities. 2) To nest the above into a case study of the FLA OHT, examining processes for centering Indigenous perspectives and priorities in systems evaluation. Study Design: Case study method involving community-based participatory research (CBPR), and using principles of Ownership, Control, Access, Possession (OCAP) in ethical Indigenous health research. The research team involves Indigenous members of FLA OHT working groups and tables, other partners and stakeholders from the broader Indigenous communities of the region, Queen’s University (Kingston, Ontario) researchers on the FLA OHT’s evaluation support structure, and other FLA OHT team members. Data used for the case study include focus groups and interviews with Indigenous members, analysis of meeting minutes and other relevant organizational documents, and surveys on team function and Indigenous-specific evaluation activities. Indigenous-specific indicators have been identified through rapid literature reviews and are being incorporated into logic models for evaluation of FLA OHT projects. Expected Outcomes: 1) A framework to operationalize collaboration between Indigenous stakeholders and mainstream health systems, in the co-design and co-execution of health system evaluation approaches that reflect Indigenous perspectives and priorities. 2) Incorporation of Indigenous-specific evaluation activities for FLA OHT projects. Results: Work in progress at time of abstract submission. Conclusions: The framework produced will serve as a practical, operationalized guide to other mainstream health systems and Indigenous groups seeking to collaborate together. The Indigenous-specific evaluation activities will enable thoughtful and rigorous consideration of whether the needs of Indigenous patients and communities are met, and allow for modification of programs and services – per the “learning health system” goal of iterative knowledge production and action to drive continuous improvement. Additionally, this project will strengthen relationships between the region’s mainstream service organizations, policymakers, Queen’s University academic researchers, and local Indigenous peoples. Strengthened relationships will facilitate current and future collaborative work to improve systems, as a step towards addressing health inequities facing Indigenous peoples.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.050 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".