Elevating the uses of storytelling approaches within Indigenous health research: a critical and participatory scoping review protocol involving Indigenous people and settlers
Bibliographic record
Abstract
BACKGROUND: There is a complicated and exploitative history of research with Indigenous peoples and accompanying calls to meaningfully and respectfully include Indigenous knowledge in healthcare. Storytelling approaches that privilege Indigenous voices can be a useful tool to break the hold that Western worldviews have within the research. Our collaborative team of Indigenous and non-Indigenous researchers, and Indigenous patients, Elders, healthcare providers, and administrators, will conduct a critical participatory, scoping review to identify and examine how storytelling has been used as a method in Indigenous health research. METHODS: Guided by two-eyed seeing, we will use Bassett and McGibbon's adaption of Arksey and O'Malley's scoping review methodology. Relevant articles will be identified through a systematic search of the gray literature, core Indigenous health journals, and online databases including Scopus, MEDLINE, Embase, CINAHL, AgeLine, Academic Search Complete, Bibliography of Native North Americans, Canadian Reference Centre, and PsycINFO. Qualitative and mixed-methods research articles will be included if the researchers involved Indigenous participants or their healthcare professionals living in Turtle Island (i.e., Canada and the USA), Australia, or Aotearoa (New Zealand); use storytelling as a research method; focus on healthcare phenomena; and are written in English. Two reviewers will independently screen titles/abstracts and full-text articles. We will extract data, identify the array of storytelling approaches, and critically examine how storytelling was valued and used. An intensive collaboration will be woven throughout all review stages as academic researchers co-create this work with Indigenous patients, Elders, healthcare professionals, and administrators. Participatory strategies will include four relational gatherings throughout the project. Based on our findings, we will co-create a framework to guide the respectful use of storytelling as a method in Indigenous health research involving Indigenous and non-Indigenous peoples. DISCUSSION: This work will enable us to elucidate the extent, range, and nature of storytelling within Indigenous health research, to critically reflect on how it has been and could be used, and to develop guidance for the respectful use of this method within research that involves Indigenous peoples and settlers. Our findings will enable the advancement of storytelling methods which meaningfully include Indigenous perspectives, practices, and priorities to benefit the health and wellbeing of Indigenous communities. SYSTEMATIC REVIEW PROTOCOL REGISTRATION: Open Science Framework ( https://osf.io/rvf7q ).
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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 | 0.411 | 0.347 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.029 | 0.020 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".