L’entretien de groupe en ligne, exemplification d’une méthode qualitative d’analyse de la réflexivité
Bibliographic record
Abstract
Comment capter la complexité des idées lors d'une collecte de données quand l’accès au terrain est limité ? La littérature actuelle sur les méthodes en ligne ou numériques traite peu de cette question, étant davantage consacrée à la description des enjeux techniques rencontrés. Afin de documenter les conditions favorables à ce type de collecte de données, 38 professionnel.le.s en santé et services sociaux ont participé à des entretiens de groupe en ligne, accompagnés d’un suivi individuel. Les résultats de l’analyse de contenu effectuée démontrent que l’entretien de groupe en ligne synchrone est une méthode pertinente pour capter le processus réflexif d’un groupe. L’attention portée au climat d’échange, à la diminution des rapports de pouvoir et l’accompagnement individualisé sont des conditions favorables à une collecte de données efficace.
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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 | 0.072 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".