Interprofessional Education in the Internal Medicine Clerkship: Results From a National Survey
Bibliographic record
Abstract
PURPOSE: Growing data support interprofessional teams as an important part of medical education. This study describes attitudes, barriers, and practices regarding interprofessional education (IPE) in internal medicine (IM) clerkships in the United States and Canada. METHOD: In 2009, a section on IPE was included on the Clerkship Directors in Internal Medicine annual survey. This section contained 23 multiple-choice questions exploring both core and subinternship experiences. Data were analyzed using descriptive statistics and Rasch analysis. RESULTS: Sixty-nine of 107 institutional members responded to the survey (64% response rate). Approximately 68% of responding clerkship directors believed that IPE is important to the practice of IM. However, only 57% believed that it should become a part of the undergraduate clinical curriculum. The three most significant barriers to IPE in the IM clerkship were scheduling alignment, time in the existing curriculum, and resources in time and money. Although more than half of respondents felt IPE should be included in the clinical curriculum, 81% indicated that there was no formal curriculum on IPE in their core IM clerkship, and 84% indicated that there was no formal curriculum during IM subinternship rotations at their institution. CONCLUSIONS: There is limited penetration of IPE into one of the foundational clinical training episodes for medical students in Liaison Committee for Medical Education-accredited schools. This may be related to misperceptions of the relative value of these experiences and limitations of curricular time. Learning in and from successful models of interprofessional teams in clinical practice may help overcome these barriers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".