Quality Improvement in Medical Education: Implications for Curriculum Change
Notice bibliographique
Résumé
To the Editor: Logically, it makes sense to begin training future physicians in quality improvement (QI) during their undergraduate and postgraduate studies. It is understandable, however, that finding a time and place to teach QI during these years can be challenging. Some might argue that teaching QI is not necessarily a priority, or cannot be done in a setting where there is already so little space and time to get the “core content” taught. I propose that the teaching of QI be integrated throughout medical training. QI should certainly be introduced as part of the core content, but it need not be isolated from the remainder of the medical curriculum. Information and knowledge that is taught should be constantly reinforced through various opportunities and courses. For example, an institution could begin with teaching the core principles of QI in the first year of medical school, and then allow students to think of a potential QI project they are interested in doing. Students can be motivated to choose a topic that is related to what they are learning at the time. For example, if the current core content focuses on cardiac diseases and management, students can explore QI initiatives that increase screening for dyslipidemia, educate patients on risk factors, etc. By integrating the QI education into the existing curriculum, medical schools can help students realize the importance of applying critical-thinking and problem-solving skills in the field of health care; this promotes their development as medical experts who are prepared for future practice. As a current medical student, I strongly believe in the power of QI as a means for physicians to improve the health care system and support their patients. Being educated about QI in the undergraduate medical curriculum would allow me and my fellow students to be exposed to opportunities in which we are able to see the applicability of QI not only as future physicians but also as current medical students. Placing the QI curriculum in context with the rest of the medical core content to be taught would make this more relevant and would allow students to integrate their learning from various contexts. Early exposure and training in QI in medical school would also allow students to develop a passion for QI and to understand how it can be applied in the future. I strongly advocate for universities to consider these approaches to teaching QI during the undergraduate (and postgraduate) years, as the benefits to learners are powerful—both currently as medical students, and in the future as health care providers. MarinaAbdel MalakSecond-year medical student, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada; [email protected]
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 | 0,032 | 0,200 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,021 | 0,028 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,003 |
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 ».