Understanding Indigenous Health Literacy Through Community-Led Engagement
Bibliographic record
Abstract
Context: This is the last in a four-part series that describes the outcome of a mixed-methods participatory social justice (MMPSJ) research project. A community engagement model was designed by participants as a synthesis of working with urban Indigenous peoples living on Treaty Six Territory and traditional homeland of the Metis in Saskatchewan, Canada. It responds to the Truth and Reconciliation Commissions calls to Action 10, 18-20. Community-based participatory health research (CBPHR) often sees the community as a place to undertake research in; this research saw the community as providing the leadership for the research. Objective: To show how MMPSJ work can help to shift from community-based to community-led research. Design: Mixed-methods participatory social justice and community-based participatory health research. Participants: Twelve Indigenous people representing four intergenerational families were invited to two Talking Circles to respond to questions derived from the Aboriginal Regional Health Surveys; as well as answer the following questions: What are the current connections between literacy and health within urban Indigenous families? What literacy issues continue to marginalize the community? How would you like this knowledge disseminated? This research was reviewed and approved by the University of Saskatchewan’s Behavioural REB. Results/Findings: Knowledge of Treaty Six teachings was increased; participants described the social justice/transformative nature of this work an opportunity to be seen well and whole; and the Community Engagement Model evolved within the MMPSJ design. Conclusions: Community-based research can be transformed to being community-led through careful consideration of power and authentically engaging with the community at each step in the process.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".