Addressing musculoskeletal curricular inadequacies within undergraduate medical education
Bibliographic record
Abstract
Abstract Background Musculoskeletal (MSK) injuries and diseases place a significant burden on the health care system. Despite this, research indicates that physician training in the area of MSK medicine has historically been inadequate, with a majority of medical students feeling that their training in MSK medicine is lacking. The goal of this investigation was to evaluate the efficacy of a new preclinical MSK curriculum that was implemented within a nationally accredited allopathic medical program. Methods Retrospective analysis was completed on five consecutive years (2017–2021) of preclinical MSK curricular data for 549 medical students, including mid and end-of-course examinations and end-of-course student satisfaction surveys. Both parametric and non-parametric methods of analysis were used to examine within and between class differences (P < 0.05). Results The new MSK curriculum covered 15 of 16 “core or must know” topics in MSK medicine, and academic performance was consistently high over the 5-year period of analysis (final course marks ranged from 76.6 ± 7.1 to 81.4 ± 8.1; failures/year: range from 0 to 4), being equal or above levels of student performance observed for other courses delivered during preclinical studies. Likert data from end-of-course surveys demonstrated that feedback was overwhelmingly positive (overall course satisfaction ranged from a low of 3.07/4.00 to a high of 3.56/4.00) and indicated that students felt that the new preclinical MSK curriculum did effectively support medical student learning and knowledge retention. Conclusion Results are expected to help advance the current body of knowledge that is dedicated to improving physician learning and knowledge retention in the area of MSK medicine and provides a curricular model that could be used by other nationally accredited medical programs to help enhance MSK learning at the preclinical levels of physician training.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".