Navigating Tensions of Efficiency and Caring in Clerkship: A Qualitative Study
Bibliographic record
Abstract
Phenomenon: Clerkship is a challenging transition during which medical students must learn to navigate the responsibilities of medical school and clinical medicine. We explored how clerks understand their roles as both medical learners and developing professionals and some of the tensionss that arise therein. Understanding how the clinical learning environment shapes the clerkship role can help educators foster compassionate care. Approach: We conducted 5 focus groups and 1 interview with 3rd-year medical students (n = 14) at University of Toronto between January and June 2016 regarding the perceived role of the clerk, compassionate care, assessment and feedback. Data were analyzed thematically. Findings: In addition to transitioning to a new learning environment, clerkship students assume different roles in response to complex and often competing expectations from preceptors. We identified three main themes: learning to impress preceptors with varying expectations, providing compassionate care—sometimes supported by preceptors, other times being secondary to efficiency—and passing assessments that required a different skill set than simply being a “good clerk.” Insights: Clerks perceive their role as providing compassionate care to patients and balance this with fulfilling the (sometimes) competing roles of being a student and developing medical professional. In a system where efficiency is often prioritized, medical students are afforded an opportunity to help satisfy the demand for greater compassion in patient-centered care.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".