Three days together around the table: using the group analysis method to value the expertise and lived experiences of key voices to innovate solutions
Bibliographic record
Abstract
Purpose This paper presents the use of the Group Analysis Method (GAM), an innovative method developed in a francophone context, to discuss issues related to the services offered in the field of addiction in Quebec’s Indigenous communities and to identify perspectives for innovative solutions. Design/methodology/approach This article begins with a detailed description of the method’s phases and steps based on the French-language writings of the developers of the GAM. The authors then illustrate a concrete example of how this method has been applied to addiction intervention stakeholders in Indigenous communities in Quebec (Canada), highlighting the type of results possible. Findings The strengths and weaknesses of the GAM for addressing sensitive issues in an Indigenous context are discussed. Recommendations for further integration of the Indigenous perspective into the approach are proposed. Originality/value This article presents a relevant qualitative method for co-constructing solutions with groups which, to our knowledge, has not been described in the English-language literature. In the light of their experience in an Indigenous context, the authors adopt a critical perspective, demonstrating the relevance of the method and suggesting adaptations to ensure an equitable distribution of power through the process.
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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 | 0.040 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".