Musculoskeletal Medicine in Undergraduate Medical Education
Bibliographic record
Abstract
Musculoskeletal (MSK) instruction has been identified as being inadequate in undergraduate medical education around the world. Just over two decades ago, there began to be recognition by medical education institutions that learners were emerging from their training lacking in both sufficient confidence and knowledge to manage MSK conditions. This was reflected in low passing rates on validated MSK knowledge assessments and in various evaluations that reported that primary care physicians, residents, and medical students generally had low confidence in their ability to accurately diagnose and treat MSK-related complaints. These gaps were linked back to problems at the undergraduate level of training, and barriers to implementing comprehensive MSK instruction were identified as a lack of time and resources dedicated to this subject area. Despite this recognized issue, little work has been done to reform Canadian medical school’s MSK curricula and identify sustainable solutions. Thus, we used the Context, Input, Process, and Product program evaluation framework situated within a sequential exploratory mixed methods approach to develop, implement, and evaluate a novel self-directed learning tool for MSK medicine at the DeGroote School of Medicine. First, a qualitative interpretive description study was used to assess student and faculty perceptions of the strengths and weaknesses of the MSK curriculum and inform the development of the learning tool. Next, a two-groups pre-test post-test design and a cross-sectional survey were used to evaluate the implementation and efficacy of the learning tool in helping medical students learn MSK medicine. Ultimately, this thesis outlines methods for evaluating MSK curricula and provides a promising learner-informed tool for assisting students in learning about MSK medicine in clinical settings.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".