Qualitative Health Research Involving Indigenous Peoples: Culturally Appropriate Data Collection Methods
Bibliographic record
Abstract
Historically, health research involving Indigenous peoples has been fraught with problems, including researchers not addressing Indigenous research priorities and then subsequently often failing to utilize culturally appropriate methods. Given this historical precedence, some Indigenous populations may be reluctant to participate in research projects. In response to these concerns, the Government of Canada has developed the Tri-Council Policy Statement (TCPS2): Research Involving the First Nations, Inuit and Métis Peoples of Canada, which stipulates the requirements for research collaborations with Indigenous communities. Utilizing this policy as an ethical standard for research practices, this paper describes, critiques and synthesizes the literature on culturally appropriate oral-data collection methods, excluding interviews and focus groups, for use with Indigenous people in Canada. Results suggest that photovoice, symbol-based reflection, circles and story-telling can be methodologically rigorous and culturally appropriate methods of collecting data with this population. Suggestions are made for researchers wishing to use these methods to promote respectful and collaborative research partnerships with Indigenous peoples in Canada.
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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 | 0.218 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".