Development of Integrated Clinical Skills and Musculoskeletal Modules
Bibliographic record
Abstract
Many students find memorizing MSK materials difficult and daunting, and often find themselves feeling overwhelmed by the number of muscles, bones, innervations, vessels and special tests that they must commit to memory. The overall objective of this study was to encourage students to adopt a more anatomical approach to clinical skills and also to show explicitly the relevance of musculoskeletal anatomy, thus moving away from rote memorization of physical exam skills and anatomical knowledge. In order to accomplish the goal of interactive case‐based modules we used the Articulate Engage software suite to create a module that progresses slide by slide in order to encourage a logical flow from patient presentation through to treatment. Using Articulate we were also able to add interactive labeled diagrams to review MSK anatomy, video and pictures to study clinical skills and integrated quizzes to test student knowledge. The MSK modules were evaluated objectively from the survey questionnaire results and subjectively from the comments from the results. Before the modules 48% of students felt they knew the MSK materials well or very well. After the modules 77% of students felt they knew the MSK materials well or very well. Over 95% of students surveyed strongly or very strongly wanted to see more modules like this.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".