Qualitative Health Research Involving Indigenous Peoples: Culturally Appropriate Data Collection Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.130 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it