The Current State of Musculoskeletal Clinical Skills Teaching for Preclerkship Medical Students
Bibliographic record
Abstract
OBJECTIVE: Musculoskeletal (MSK) complaints have high prevalence in primary care practice (12%-20% of visits), yet many trainees and physicians identify themselves as weak in MSK physical examination (PE) skills. As recruitment to MSK specialties lags behind retirement rates, there is a shortage of physicians able to effectively teach this subject. We investigated current practices of Canadian undergraduate medical programs regarding the nature, amount, and source of preclerkship MSK PE clinical skills teaching; and documented the frequency and extent that Patient Partners in Arthritis (PPIA) are used in this educational setting. METHODS: A 2-page self-administered electronic questionnaire combining open- and close-ended questions was developed and piloted. It was distributed by e-mail to all Canadian undergraduate associate-deans and to 16/17 undergraduate MSK course organizers. RESULTS: Supervised practice in small groups and the PPIA are the most prevalent teaching methods. Objective structured clinical examinations are the most prevalent evaluation methods. The average number of hours devoted to teaching these skills is very small compared to the prevalence of MSK complaints in the population. Canadian schools' preclerkship MSK PE clinical skills teaching is heavily dependent on the contributions of non-MSK specialists. CONCLUSION: The weak link in the Canadian MSK PE educational cycle appears to be the amount of time available for students' deliberate practice with expert feedback. There is a need for methods to evaluate and further develop MSK PE teaching by non-MSK specialists. This and increased use of PPIA at the preclerkship level may provide students more time for practice with feedback.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".