L’utilisation de « groupes de discussion » dans la recherche en déficience intellectuelle
Bibliographic record
Abstract
Cet article est le premier de deux articles portant sur une étude qui a pour but d’identifier les meilleures pratiques pour l’utilisation du groupe de discussion focalisée en général, puis avec des participants ayant une déficience intellectuelle en particulier. Le premier article présente les meilleures pratiques répertoriées lors de la recension des écrits. Les résultats de cette recension sont regroupés en cinq dimensions : le recrutement des participants, le consentement à la recherche, la structure des rencontres, l’animation des rencontres ainsi que leur contenu. Les résultats proposent des aménagements aux façons de faire traditionnelles du groupe de discussion focalisée afin de les rendre plus accessibles à une diversité de participants et de faciliter l’expression de leur point de vue.
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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 | 0.133 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".