Teaching the Musculoskeletal Examination: Are Patient Educators as Effective as Rheumatology Faculty?
Bibliographic record
Abstract
BACKGROUND: Effective education of clinical skills is essential if doctors are to meet the needs of patients with rheumatic disease, but shrinking faculty numbers has made clinical teaching difficult. A solution to this problem is to utilize patient educators. PURPOSE: This study evaluates the teaching effectiveness of patient educators compared to rheumatology faculty using the musculoskeletal (MSK) examination. METHOD: Sixty-two 2nd-year medical students were randomized to receive instruction from patient educators or faculty. Tutorial groups received instructions during three, 3-hr sessions. Clinical skills were evaluated by a 9 station objective structured clinical examination. Students completed a tutor evaluation form to assess their level of satisfaction with the process. RESULTS: Faculty-taught students received a higher overall mark (66.5% vs. 62.1%,) and fewer failed than patient educator-taught students (5 vs. 0, p = 0.02). Students rated faculty educators higher than patient educators (4.13 vs. 3.58 on a 5-point Likert scale). CONCLUSION: Rheumatology faculty appear to be more effective teachers of the MSK physical exam than patient educators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".