Notice bibliographique
Résumé
We agree with Webster’s call for more precise models of clinical decision making (CDM). The question is: What is meant by precision? High levels of analytic precision can readily be achieved in Type 2 processing, but overall precision, of the type seen in well-calibrated CDM, is what is needed. Importantly, the role played by Type 1 processing, which has received relatively little emphasis, needs full acknowledgment. Current dual process theory and neurophysiologic testing of the model have progressed considerably beyond earlier concepts of the conscious–unconscious mind. If the external validity of the two studies referred to by Webster is unknown, then it is difficult to see how any conclusions can be other than tenuous. Aside from the very nonclinical conditions under which Norman and colleagues’1 experiments were conducted, we questioned whether they had any bearing on Type 1 processing, as it remains unclear if the experimental designs involved other than Type 2 processing. To Webster’s next point, Type 1 processing is associative and often no more than autonomous reflexivity, so we should not refer to it as a “mode of thinking”; thinking implies a more deliberate process. Slowing down may not switch off Type 1 processes (although it can), but hopefully it may lead to reevaluation of the conclusions that result from them. Moreover, it seems that how people slow down is important. Mamede et al2 have demonstrated the improvements that result from structured reflection. Overall, the refinement of CDM skills comes from repeated practice—the intentional application of knowledge that leads to a decision, followed by reflection upon that process, and on its outcome. Structured feedback enriches the experience of reflection and the learning that results from it. It would be a practical impossibility for clinicians to evaluate every Type 1 decision—many are essential to well-calibrated CDM and most should be left alone; some, however, will need challenging. Further, we doubt that anyone seriously believes that exhorting doctors to try or think harder are solutions to diagnostic failures. Invariably, better-calibrated CDM is not a matter of effort but, rather, an understanding of how the process works, and the important equipoise of Type 1 and Type 2 processing. Pat Croskerry, MD, PhD Professor and director, Critical Thinking Program, Division of Medical Education, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; [email protected] David A. Petrie, MD Professor of emergency medicine and professor, Department of Emergency Medicine, Faculty of Medicine, Dalhousie University, and chief, Capital District Health Authority Department of Emergency Medicine, Halifax, Nova Scotia, Canada. James B. Reilly, MD, MS Director, Internal Medicine Residency, Allegheny General Hospital, Western Pennsylvania Hospital Educational Consortium, Pittsburgh, Pennsylvania, and assistant professor of medicine, Temple University School of Medicine, Philadelphia, Pennsylvania. Gordon Tait, PhD Assistant professor, Departments of Surgery and Anesthesia, and staff scientist, Department of Anesthesia, Toronto General Hospital, University Health Network, Faculty of Medicine, University of Toronto, Toronto, Ontario, 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 | 0,009 | 0,082 |
| 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 | 0,008 | 0,014 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,033 | 0,083 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,013 |
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 ».