Elevating the Uses of Storytelling Methods Within Indigenous Health Research: A Critical, Participatory Scoping Review
Bibliographic record
Abstract
There is a profoundly troubling history of research being done on Indigenous peoples without regard for their priorities and accompanying calls to decolonize health research. Storytelling methods can privilege Indigenous voices in research. Indigenous people’s knowledge systems have existed for millennium, where knowledge is produced and shared through stories. Our collaborative team of Indigenous and non-Indigenous researchers, and Indigenous Elders, patients, healthcare providers, and administrators, conducted a participatory, scoping review to examine how storytelling has been used as a method in Indigenous health research on Turtle Island (North America), Australia, and Aotearoa (New Zealand). We searched key databases and online sources for qualitative and mixed-methods studies that involved Indigenous participants and used storytelling as a method in health research. Reviewers screened abstracts/full texts to confirm eligibility. Narrative data were extracted and synthesized. An intensive collaboration was woven throughout and included gatherings incorporating Indigenous protocol, Elders’ teachings on storytelling, and sharing circles. We included 178 articles and found a diverse array of storytelling approaches and adaptations, along with exemplary practices and problematic omissions. Researchers honoured Indigenous ways of knowing, being, and doing through careful preparation and community engagement to do storywork, inclusion of Indigenous languages and protocols, and Indigenous initiation and governance. Storytelling centered Indigenous voices, was a culturally relevant and respectful method, involved a healing process, and reclaimed Indigenous stories. But it could result in several challenges when researchers did not meaningfully engage with Indigenous peoples. These findings can guide respectful storytelling research that bridges divergent Indigenous and Western knowledge systems, to decolonize health research.
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 | 0.326 | 0.458 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.044 | 0.029 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier 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".