Beyond Workarounds: Enhancing Education, Care, and Wellness on Inpatient Medicine Rotations —A Multicenter Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Inpatient medicine rotations (IMRs) aim to deliver exceptional clinical education and high-quality patient care. However, increasing workloads and the fast pace of inpatient wards are undermining this dual objective. OBJECTIVE: To explore tensions and challenges between balancing education and clinical practice on IMRs and how physician-leaders are addressing them. DESIGN: Constructivist grounded theory. PARTICIPANTS: Inpatient medicine rotation physician-leaders from academic medical centers in the United States and Canada. APPROACH: Data collection involved semi-structured individual and group interviews, collected and analyzed iteratively to develop an explanatory conceptual model. Rigor was enhanced through constant comparison, investigator triangulation, and return-of-findings sessions. KEY RESULTS: Twenty interviews involving 27 participants from 20 distinct training programs were conducted. Participants endorsed IMRs unique clinical and educational value. However, they flagged how increasing workloads and resource challenges produce tensions that can undermine the quality of both which, as a consequence, negatively impact attending and trainee wellness. While reactionary "workarounds" were the norm, they often created unanticipated problems. Key IMR features and strategies for success were identified and organized into six categories: (1) patient mix/census; (2) leadership collaboration; (3) collaborative care models; (4) rotation scheduling; (5) clinical workflow; (6) educational workflow. How physician-leaders configured their IMR structures and processes within these categories had the potential to support or undermine the delivery of high-quality care, education, and wellness. CONCLUSIONS: Inpatient medicine rotations, which is essential for clinical care and education, are currently facing serious challenges from a changing clinical and educational landscape. Our findings present a conceptual model highlighting key modifiable variables, giving physician-leaders a framework to assess and enhance their IMR's clinical learning environment, thus fostering quality care, education, and clinician wellness.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".