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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".