Notice bibliographique
Résumé
To the Editor: Ginsburg and colleagues1 touch the tip of the iceberg in their recent article examining physicians’ responses to professional challenges. The authors analyzed physicians’ explanations of why they would make “wrong” decisions in response to dilemmas likely to occur in medical practice and found that participants’ desire to be helpful was a frequent rationale. If being seen as helpful requires decisions that are inconsistent with standard of care guidelines, perhaps we should question the usefulness of such guidelines. Of equal importance, yet not emphasized by Ginsburg and colleagues (because it was not as prominent as other factors they identified), is the role of social pressure as an influence on physicians’ decision making. The physicians in this study referred to the actions of other doctors when making decisions about dilemmas—as Ginsburg and colleagues note, “Participants often made reference to ‘what the other doctors’ would do, even while admitting that they do not always know what other doctors would actually do.” To use other doctors as reasons for less-than-professional practice seems, well, unprofessional. These subtle social pressures occur far more often in everyday practice. For example, we recently attended a medical grand rounds session, during which a respected faculty member shared a patient history with an unequivocal diagnosis and management plan. However, in the interest of seeing how the audience would respond, he gave a “wrong” diagnosis. This was followed by a series of three questions posed to the audience and an invitation to respond by using polling clickers for anonymity. The polling percentages showed the audience’s alignment with the incorrect diagnosis, with 48% choosing a diagnostic step, 30% choosing a first line of therapy, and 62% choosing a disposition and consultation that would be consistent with the incorrect diagnosis. Are physicians using one another as a safety net or as a means of absolving personal responsibility? There are many similar anecdotes, and empirical studies are emerging too.2 With the goal of evolving into a high-reliability organization driven by a focus on patient safety and high-quality decision making, findings such as those reported by Ginsburg and colleagues demonstrate that justifying and rationalizing a mind-set for conforming to the need to be seen as helpful to our patients, friends, colleagues, and the general public is not helpful at all. Ghazwan Altabbaa, MD, MSc Clinical associate professor and associate program director, Internal Medicine Residency Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada; [email protected] Tanya Beran, PhD Professor of medical education, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. Alyshah Kaba, MSc PhD candidate, medical education and research, Department of Community Health Sciences, Cumming School of Medicine, W21C Research and Innovation Centre, University of Calgary, Calgary, Alberta, 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 | 0,006 | 0,063 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,013 | 0,014 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,204 | 0,119 |
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 ».