Development of Effective Learning Modules to Integrate the Anatomy and Clinical Skill of Musculoskeletal System
Bibliographic record
Abstract
Development of Effective Learning Modules to Integrate the Anatomy and Clinical Skill of Musculoskeletal System 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. The focus of this project was to develop effective learning modules that will help students learn musculoskeletal materials and enhance their education environment. In order to accomplish the goal of interactive modules we used the Medicine and Dentistry Integrated Curriculum Online (MEDICOL) Vista to create two modules that integrated the anatomy and clinical skill of musculoskeletal system. Using the MEDICOL we were able to add interactive labeled diagrams to review MSK anatomy 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 55% of students felt they knew the MSK materials well or very well. After the modules 83% of students felt they knew the MSK materials well or very well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".