Strategies for Educational Exposure to Acute Care in Longitudinal Clerkships
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".