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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".