Innovation in qualitative interviews: “Sharing Circles” in a First Nations community: Table 1
Bibliographic record
Abstract
There is growing recognition that different research approaches are necessary to understand the complex interaction between individual and social processes that contribute to risk-taking and injuries. Therefore, qualitative studies have an important role in injury prevention research. This article describes qualitative research in general and outlines some of the ways qualitative research can add to our understanding of injury. It also describes the role, format and methods of interviews (person-to-person and focus groups) commonly performed in qualitative studies, and proposes a novel approach to interviewing that has special relevance and value in injury research with indigenous populations. This methodology adapts focus group methods to be consistent with the goals and procedures of the traditional First Nations communities' Sharing Circles. This adaptation provides a culturally appropriate and sensitive method of developing a deep and broad understanding of indigenous participants' verbal descriptions of their feelings, their experiences and their modes of reasoning. After detailing of this adaptation of the Sharing Circle as a vibrant and vital interview and analysis method, the use of Sharing Circle interview methodology will be illustrated in a study investigating how an Alberta First Nations community experiences and deals with disproportionate levels of injuries arising from impaired driving, outlining important findings uncovered using this novel interviewing method. These findings have been informative to First Nations communities themselves, have informed policy makers provincially and nationally, and have instigated culturally appropriate intervention techniques for Canadian First Nations communities.
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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 | 0.050 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".