Australian medical students report poor confidence managing common orthopaedic sports‐related injuries: findings of a multi‐site survey
Bibliographic record
Abstract
It has been estimated that musculoskeletal issues comprise around 15–30% of primary care visits in the United States and Canada,1 and an Australian audit identified that 17% of presentations for injuries to Victorian emergency departments from 2012–2013 to 2014–2015 were related to sport or active recreation.2 Furthermore, a study of older adults identified that a majority of emergency department presentations related to a musculoskeletal diagnosis.3 These factors translate to a demand for medical schools to produce graduates with a strong foundation in assessing and managing common musculoskeletal injuries. However, with the continued evolution of medical school curriculum structure, the ability to meet student expectations and provide adequate education in orthopaedics and sports medicine is becoming increasingly challenging. Indeed, several surveys of medical students in the United States have revealed consistent themes recognizing the importance of musculoskeletal education but a lack of clinical confidence and competence in the area.4, 5 Therefore, the aim of this study was to assess the perceptions of Australian medical students regarding their exposure to, and quality of education in, orthopaedic sports medicine, the perceived importance of the field, and confidence in managing common sports-related symptoms and injuries.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".