Physiotherapist-led musculoskeletal education: an innovative approach to teach medical students musculoskeletal assessment techniques
Bibliographic record
Abstract
Implication Statement We developed physiotherapist-led interprofessional (IP) workshops for medical students each dedicated to a specific anatomical region. The workshops alternated between theoretical presentations from a physiotherapist about basic musculoskeletal (MSK) assessment techniques and hands-on practice in small groups with feedback from Master's-level physiotherapy students (MPT). The workshops created an optimal IP learning environment where medical students can learn MSK assessment techniques and MPTs can apply their knowledge and clinical skills. Academic institutions with physiotherapy and medical programs seeking to develop IP learning activities should foster opportunities for IP collaboration between physiotherapy and undergraduate medical education. Énoncé des implications de la recherche Nous avons créé, à l'intention des étudiants en médecine, des ateliers interprofessionnels (IP) dirigés par un physiothérapeute, chacun axé sur une région anatomique spécifique. Des ateliers théoriques sur les méthodes d'évaluation de base de l'appareil musculo-squelettique (MS) sont donnés en alternance avec des travaux pratiques en petits groupes, lors desquels des étudiants à la maîtrise en physiothérapie (M. Pht.) offrent une rétroaction aux participants. Les ateliers fournissent un environnement optimal pour l'apprentissage IP qui permet aux étudiants en médecine de se familiariser avec les méthodes d'évaluation de l'appareil MS, et aux étudiants M. Pht. d'appliquer leurs connaissances et leurs habiletés cliniques. Les établissements d'éducation offrant des programmes de physiothérapie et de médecine de premier cycle qui cherchent à mettre en place des activités d'apprentissage IP devraient favoriser la collaboration entre ces deux programmes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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 teacher head, 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".