60 Optimizing overdiagnosis education for medical students
Notice bibliographique
Résumé
Objectives Understanding overdiagnosis is difficult for healthcare workers, both in theory and in practice. At it’s core it turns people into patients needlessly by medicalizing everyday occurrences or by expanding disease definitions. For trainees, the terms ’false positive’ and ’misdiagnosis’ are commonly conflated with ’overdiagnosis.’ Although medical schools in Canada provide some instruction in the fundamentals of clinical epidemiology, overdiagnosis and overtreatment are typically ignored as objectives in undergraduate medical curriculum. For example, Toronto Notes is regarded as a comprehensive resource for students learning about medicine and preparing for the Medical Council of Canada Qualifying Examination (MCCQE), but the term ’overdiagnosis’ only appears three times in the textbook – only in connection with prostate-specific antigen screening. To increase students’ understanding of the concepts and demonstrate how to apply them in subsequent training and practice, this must be expanded and generalised, and instructional techniques must be developed. Method The major goals of this workshop will be to pinpoint the key components of overdiagnosis education and the best pedagogical strategies for imparting to undergraduate medical students the ideas and issues at the heart of overdiagnosis. Medical educators with expertise in teaching overdiagnosis will participate in the workshop together with medical students. Broader examples will be sought for teaching undergraduate medical students about the concepts and issues surrounding overdiagnosis. A list of topics and themes in overdiagnosis and overtreatment will be compiled to inform a curriculum on the subject. The session will present current methods and get feedback on what may be the best techniques to teaching medical students about overdiagnosis and preventing them from overdiagnosing as future physicians. Methods for disseminating information through problem-based learning or small-group discussions will be proposed. This will promote awareness and understanding of overdiagnosis and emphasize patient-centered care through collaborative decision-making. Results A list of themes in overdiagnosis and overtreatment will be compiled in order to inform a curriculum on the subject for medical students. Also, methods for disseminating information through problem-based learning or small-group discussions will be proposed. This will support both the promotion of awareness and understanding of overdiagnosis and an emphasis on patient-centered care through collaborative decision-making. The major goals of this workshop will be to pinpoint the key components of overdiagnosis education and the best pedagogical strategies for imparting to undergraduate medical students the ideas and issues at the heart of overdiagnosis. The deliberations and input from this workshop will inform a paper on the topic that will be submitted to a medical education journal. Conclusions The outcomes of this workshop can be used to enhance the CanMEDS 2025, which is currently being developed. CanMEDS is a framework that identifies and defines the skills doctors need to effectively address the patients‘ healthcare needs. Medical students will be better equipped to undertake good care, and identify substandard care if they are more aware of potential for overdiagnosis, and informed of the advantages and limitations of treatments and procedures. The deliberations and input from this workshop will inform a paper on the topic that will be submitted to a medical education journal.
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 | 0,008 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,060 | 0,022 |
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 ».