Digital Storytelling as a Patient Engagement and Research Approach With First Nations Women: How the Medicine Wheel Guided Our Debwewin Journey
Bibliographic record
Abstract
When research is conducted from a Western paradigm alone, the findings and resultant policies often ignore Indigenous peoples’ health practices and fail to align with their health care priorities. There is a need for decolonized approaches within qualitative health research to collaboratively identify intersecting reasons behind troubling health inequities and to integrate Indigenous knowledge into current health care services. We engaged with First Nations women to explore to what extent digital storytelling could be a feasible, acceptable, and meaningful research method to inform culturally safe health care services. This novel approach created a culturally safe and ethical space for authentic patient engagement. Our conversations were profound and provided deep insights into First Nations women’s experiences with breast cancer and guidance for our future qualitative study. We found that the digital storytelling workshop facilitated a Debwewin journey, which is an ancient Anishinabe way of knowing that connects one’s heart knowledge and mind knowledge.
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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 | 0.036 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".