An Innovative Sequential Focus Group Method for Investigating Diabetes Care Experiences With Indigenous Peoples in Canada
Bibliographic record
Abstract
This article describes the innovative use of sequential focus groups (SFGs) with Indigenous adults living with type 2 diabetes. This use of SFGs has not been previously described in the literature. In our project, SFGs were used to explore Indigenous people’s experiences in managing their diabetes. Our research objective has been to elucidate deep understandings of these experiences in order to inform the development of continuing medical education curriculum with the aim of improving approaches to diabetes care for Indigenous people. Working in partnerships with Indigenous health organizations, we recruited four groups comprising participants from diverse Indigenous communities (two urban, two rural) in three provinces of Canada. We conducted a series of five focus groups (SFGs) with the same participants (6–8 participants) at each site for a total of 20 focus groups and 29 participants. Indigenous people living with type 2 diabetes were asked open-ended questions concerning their experiences with diabetes and diabetes care in primary health-care settings. Our findings concerning the use of SFGs for Indigenous health research draw on team member and participants’ reflections captured in facilitator field notes, memos from debriefing sessions, and focus group transcripts. The SFG approach enabled in-depth exploration of the complex, and at times sensitive, issues related to Indigenous people’s views on diabetes and their experiences of diabetes care. The repeated sessions facilitated comfort and camaraderie among participants, which led to insightful sessions filled with personal and emotional stories of living with diabetes, the impacts of colonization, and health-care experiences. Overall, the method fostered a deeper level of engagement, exploration, and reflection than a single-session focus group typically would. We suggest this adaptation of the traditional single-session focus groups would be applicable to a wide variety of research concerning sensitive health topics with vulnerable populations.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".