When Should Students Learn Essential Physical Examination Skills? Views of Internal Medicine Clerkship Directors in North America
Bibliographic record
Abstract
PURPOSE: To determine whether any consensus exists among internal medicine clerkship directors regarding when students should acquire proficiency in selected physical examination (PE) skills. METHOD: In 2004, the annual survey of Clerkship Directors in Internal Medicine (CDIM) included a question about the timing of PE-skills proficiency. (CDIM members are from 123 U.S. and Canadian medical schools.) A total of 259 members (123 institutional and 136 individual members) were asked the following question about 39 common physical examination skills, selected using a consensus process among the authors and members of the CDIM Council: "When in the medical school curriculum should medical students acquire proficiency for the following skills?" RESULTS: There were 157 respondents, an overall response rate of 60%. There were 89 (72%) responding institutional members and 68 (50%) responding individual members. Respondents agreed that 31 (80%) of the skills should be learned by the end of the clerkship year. However, considerable variability existed regarding when in the curriculum those skills should be learned: for only 18 of 39 skills was there 80% agreement on skills-learning timing. CDIM members were divided on whether normal examination findings should be learned before or during the clerkships. CONCLUSIONS: Variability existed among CDIM members regarding their expectations for the timing of student physical examination learning in the undergraduate medical curriculum. Creating a common vision among clerkship directors and faculty regarding what neophyte clinicians must learn to do and when they are expected to be able to do it will help to address the issue of physical examination proficiency standards of medical students.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".