Strategies for Educational Exposure to Acute Care in Longitudinal Clerkships
Notice bibliographique
Résumé
To the Editor: In their recent article, Anderson and Powers1 propose that longitudinal integrated clerkships (LICs)—as opposed to traditional block clerkships—teach medical students to manage stable, chronic disease at the expense of acute exacerbations of these conditions. As students in an LIC program, we believe this model provides opportunities for both. Our program has used the following strategies to ensure exposure to acute care: Acutely ill patients are scheduled on an urgent basis in ambulatory clinics. Hence, students may see patients with heart failure exacerbations in a cardiology clinic or gout exacerbations in a rheumatology clinic. Students also rotate through a general internal medicine (GIM) rapid assessment clinic, where patients presenting to the emergency room, or to family medicine clinics with acute illnesses, are seen urgently. Students complete a one-month GIM inpatient block that provides extensive exposure to acute decompensation of chronic disease. They complete this rotation in addition to longitudinal ambulatory clinics. A previous study of 75 clerkship students completing an ambulatory rotation, along with one month on the inpatient GIM ward, showed no differences in knowledge, but students gained exposure to a wider variety of patient presentations than students exclusively with inpatient experience.2 In family medicine clinics, students see patients who walk in or book same-day appointments, gaining experience in managing emergent patient complaints. Emergency medicine rotations are scheduled as yearlong call shifts, where students are exposed to acute exacerbations of chronic diseases managed in ambulatory clinics. Students in both block and longitudinal clerkships are required to log key patient presentations to ensure comparable exposure to acute presenting complaints. Furthermore, Anderson and Powers1 raise the concern that medical students in LICs risk viewing ambulatory chronic care as primarily comprising nutrition counseling and titrating medications. However, many subspecialties, such as endocrinology and rheumatology, consist primarily of stable disease management in outpatient clinics, which students should consider when selecting careers. Additionally, there is increasing, system-wide recognition of the importance of preventive health promotion.2 The challenging and essential tasks of lifestyle counseling and medication management prevent destabilization of patients with chronic illnesses.3 Finally, variations in exposure to acuity may be a function of curriculum design and clinic scheduling practices, rather than an inherent characteristic of the LIC model. Hence, we respectfully disagree that LICs sacrifice opportunities for exposure to acute care, and we thank the authors for drawing attention to this important issue. Acknowledgments: The authors extend immense gratitude to Dr. Karen Weyman, chief of family medicine, St. Michael’s Hospital, Toronto, Ontario, Canada, who kindly revised this manuscript. Her guidance based on experience developing the longitudinal integrated clerkship at the University of Toronto was invaluable. Arunima SivanandThird-year medical student, Longitudinal Integrated Clerkship program, University of Toronto, Toronto, Ontario, Canada; [email protected] ca; ORCID: https://orcid.org/0000-0001-8556-3643. Sujen SaravanabavanThird-year medical student, Longitudinal Integrated Clerkship program, University of Toronto, Toronto, Ontario, Canada.
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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 | 0,010 | 0,059 |
| 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 | 0,003 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,020 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,005 |
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 ».