Inadequacies in Undergraduate Musculoskeletal Education—A Survey of Nationally Accredited Allopathic Medical Programs in Canada
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to document the current state of musculoskeletal (MSK) medicine education across nationally accredited undergraduate medical programs. DESIGN: A cross-sectional survey design was used to gather curricular data on the following three musculoskeletal themes: (1) anatomy education, (2) preclinical education, and (3) clerkship education. RESULTS: The survey had a 100% response rate with all 14 English-language medical schools in Canada responding. The mean time spent teaching musculoskeletal anatomy was 29.8 hrs (SD ± 13.7, range = 12-60), with all but one program using some form of cadaveric-based instruction. Musculoskeletal preclinical curricula averaged 58.0 hrs (SD ± 53.4, range = 6-204), with didactic lectures, case-based learning, and small group tutorials being the most common modes of instruction. Curricular content varied greatly, with only 25% of "core or must-know" musculoskeletal topics being covered in detail by all programs. Musculoskeletal training in clerkship was required by only 50% of programs, most commonly being 2 wks in duration. CONCLUSIONS: Results document the large variability and curricular inadequacies that exist in musculoskeletal education across nationally accredited allopathic programs and highlight the need for the identification and implementation of more consistent musculoskeletal curricular content and educational standards by all nationally accredited medical programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".