Methodological consideration of story telling in qualitative research involving Indigenous Peoples
Bibliographic record
Abstract
BACKGROUND: The use of storytelling in qualitative research involving Inuit compliments the oral tradition of Inuit culture. The objective of the research was to explore the use of qualitative methods to gain understanding of the experience of living with diabetes, with the ultimate goal of better formulating health care delivery and health promotion among Inuit. METHODS: In-depth interviews were analyzed and interpreted using thematic analysis, open coding, and structured narrative analysis. Inuit community members acted as partners through all stages of the research. RESULTS: ''Because the more we understand, the more we're gonna do a prevention on it ... What I want is use my, use my diabetes, what I have ... so that it can be used by other people for prevention because they'll have understanding about it'' - an Inuk storyteller speaks to the value of education in health promotion. Key methodological issues found relevant to improving qualitative research with Indigenous Peoples include: (i) participatory research methods, grounded in principals of equity, through all phases of research; (ii) the presentation of narratives rather than only interpretations of narratives; (iii) understanding of culture, language, and place to frame the interpretation of the stories in the context within which storytellers experience living with their diabetes, and (iv) the value of multiple methods of analyses. INTERPRETATION: This article comments on the challenges of conducting rigorous research in a cross-cultural setting and outlines methodologies that can improve qualitative narrative analyses research. The research highlighted experiences of living with diabetes and the ways in which storytellers coped and negotiated social support.
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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 | 0.658 | 0.582 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.024 | 0.058 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.013 | 0.025 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".