Notice bibliographique
Résumé
We appreciate Mamede and Schmidt’s thoughtful reading of our article. While they raise interesting hypotheses about methodological distinctions across studies, we do not see such differences as “conceptual and methodological shortcomings.” Instead, we think differences between their methodology and ours represent differing perspectives regarding the extent to which one can assume participants’ reasoning strategies based upon the experimental instructions they are given. The authors emphasize the importance of identifying contradictory features prior to generating diagnostic hypotheses; we are not convinced that such sequential representations of reasoning are realistic given the many decades psychologists required to generate separable measures of analytic and nonanalytic processes.1 In medicine, a series of studies have revealed that clinical features are more likely to be seen if one has the relevant diagnosis in mind,2,3 suggesting that reasoning is rarely solely inductive or deductive, and raising questions about the extent to which a clinician could ever be prevented from generating diagnoses when contemplating the features of a case. One of the definitional properties of nonanalytic reasoning is that it is fast, automatic, and largely beyond conscious control.4 This is not to say that the differences between Mamede and Schmidt’s methodology and ours could not account for the differences in results. Rather, it is meant simply to highlight one problem with labeling experimental conditions in these sorts of studies with theoretical constructs (i.e., “automatic reasoning”) rather than strictly limiting oneself to labels based on what was observably done (i.e., instructions to offer a diagnosis based on one’s first impression). The order of instructions may be important, but that is an empirical question that would need to be tested directly. Any such head-to-head comparison of processing interventions should, however, take into account a variety of methodologies suggesting that more analytic, conscious processing—in the absence of a preceding, experimentally induced bias—does not necessarily align with greater diagnostic accuracy.5,6 More generally, in testing the influence of such differences, it will be important to design interventions that are practically meaningful. We favor experimental control, but if the control is so great as to have little real-world value, then the benefit of any instructional intervention will be questionable. Rigid adherence to a reasoning protocol that requires strict researcher oversight is unlikely to be feasible for application in a naturalistic clinical setting. Jonathan S. Ilgen, MD, MCR Assistant professor, Division of Emergency Medicine, University of Washington, School of Medicine, Seattle, Washington; [email protected] Judith L. Bowen, MD Professor, Department of Medicine, Oregon Health & Science University, School of Medicine, Portland, Oregon. Kevin W. Eva, PhD Professor and director of education research and scholarship, Department of Medicine, and senior scientist, Centre for Health Education Scholarship, University of British Columbia, Vancouver, British Columbia, Canada.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,091 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,007 |
| Communication savante | 0,006 | 0,012 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,044 | 0,094 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».