Intégrer un interprete dans les consultations de médecine familiale : une analyse de discours assistée par ordinateur
Bibliographic record
Abstract
Exploramos las representaciones del trabajo con intérpretes entre médicos de familia en Quebec. Se organizaron focus groups (FG) con estudiantes de tercer año de Medicina (6, N= 22), médicos residen¬tes (4, N= 29) y médicos en ejercicio (5, N= 47). Preparamos tres vídeos con con¬sultas médicas con intérprete. Se presentó a cada FG dos de los vídeos, que se co¬mentaron de forma separada. Los resulta¬dos hacen explícitos distintos discursos. Los estudiantes parecen más preocupados por la relación médico-paciente, mientras que los médicos se concentran en la in¬formación. El discurso de los residentes señala cuestiones relacionadas con la for¬mación de la identidad. Los resultados se analizaron a la luz de la teoría de la co¬municación de Habermas.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".