Engaging Indigenous partners in health service transformation: a framework for sustained engagement built on trust
Bibliographic record
Abstract
Health research and service delivery often fail to incorporate Indigenous worldviews and local community protocols, as well as historic experiences and knowledge of harmful research practices leaving Indigenous individuals wary of participating in research. Meaningfully engaging with Indigenous stakeholders (e.g., youth, family/carers, decision-makers, and service providers) in research partnerships offers a promising pathway toward better access and quality health care and improved mental health and wellness outcomes that better meet Indigenous youths' needs. This paper traces the development of a national research network, ACCESS Open Minds, a network of youth, family members/carers, clinicians, decision-makers and academics focused on transforming youth mental health services in Canada. The context for this network is one in which diverse Indigenous stakeholders have been engaged in health systems and service transformation against the historical and ongoing backdrop of colonialism. Within this paper, we will focus on the network's past and on-going activities for engaging Indigenous partners to provide a critical lens on the partnership development process. We will also underscore key activities/reflections central to the development of trust and ultimately, the sustained engagement of Indigenous youth and community partners within mental health service transformation. Both trust development and sustained engagement are integral to building momentum in developing, implementing and evaluating health systems and service transformation in collaboration with Indigenous youth and community partners. We propose a framework for engaging Indigenous community partners and youth within service transformation. Trust is highlighted as the context, mechanism, and outcome. We conclude with the need to build an evidence base of what works and - and what does not work - in achieving and sustaining trust within the process of engaging Indigenous partners in health system transformation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".