La théorie des interactions symboliques et les enjeux de l’entrée massive des femmes en médecine
Bibliographic record
Abstract
L'entrée des femmes en médecine au Québec est susceptible d'avoir un impact sur le rôle de médecin et sur le statut de la profession. Les femmes peuvent favoriser l'humanisation de ce rôle professionnel ; elles peuvent également contribuer à l'accentuation du salariat ainsi qu'au clivage de la profession entre la médecine générale et la médecine spécialisée. Jusqu'à présent, la majorité des études portant sur le sujet a été de type empirique et n'a pas permis de cerner les facteurs d'ordre méso et macrosystémique entrant nécessairement en ligne de compte dans l'acquisition et l'évolution d'un rôle professionnel très fortement institutionnalisé comme l'est celui de médecin. Dans le cadre de cet article, l'auteure propose donc d'analyser la venue des femmes en médecine à partir de la théorie des interactions symboliques. La démarche permet de déterminer les facteurs à la base du processus d'acquisition du rôle et d'obtenir ainsi une meilleure compréhension des enjeux sous-jacents à l'entrée des femmes en médecine.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".