Musculoskeletal examination teaching by patients versus physicians: How are they different? Neither better nor worse, but complementary
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal (MSK) complaints comprise 12-20% of primary healthcare; however, practicing physicians' MSK physical examination (PE) skills are weak. Further, there is a shortage of specialists able to effectively teach this subject. Previous evaluations of patient educators have yielded mixed results. AIMS: The aim of this study is to document how teaching by patient educators and physician tutors in MSK PE skills differs. METHODS: A qualitative researcher observed, video-recorded, and took notes during preclerkship MSK PE teaching sessions given by patient educators or physician tutors. The researcher identified themes which were evaluated by collective case study methods. RESULTS: Two patient educator and four physician groups were evaluated. The patient educators were more consistent regarding content and style than the physicians. There appeared to be a continuum in teaching organization from patient educator to novice physician tutors to experienced physician tutors. The patient educators consistently covered all major joints (physicians did not); physicians were more likely to request verbalization of actions, relate findings to history, receive questions, and use opportunistic teaching moments. CONCLUSIONS: Understanding preclerkship MSK teaching by patient educators compared to physician tutors is necessary for appropriate targeting of the existing Patient Partners® in Arthritis patient educator program and to guide the development of future MSK teaching initiatives.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".