What do we know about Objective Structured Clinical Examination in Sport and Exercise Medicine? A scoping review
Bibliographic record
Abstract
Background and objectives: Despite the importance of the Objective Structured Clinical Examination (OSCE) in Sport and Exercise Medicine, the literature on the topic is fragmented and has been poorly developed. The goal of this review was to map current knowledge about how the OSCE is used in Sport and Exercise Medicine, and to identify knowledge gaps for future research. Method: The authors conducted a scoping review. They searched PubMed and Scopus for articles using key terms related to 'OSCE' and 'sport medicine' with no limit on search start date and up to July 2022. Retrieved records were imported, abstracts were screened, and full-text articles were reviewed. A forward and backward citation tracking was conducted. Data was extracted and a qualitative meta-summary of the studies was conducted. Results: = 3). Thirteen studies reported validity and/or reliability of the OSCE. Conclusion: Despite the widespread use of OSCEs in the examination of Sport and Exercise Medicine trainees, only a handful of scholarly works have been published. More research is needed to support the use of OSCE in Sport and Exercise Medicine for its initial purpose. We highlight avenues for future research such as assessing the need for a deeper exploration of the relationship between candidate characteristics and OSCE scores.
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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 | 0.024 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".