A Comparison of Sports and Exercise Medicine Training for Physicians Across Five English-Speaking Countries
Bibliographic record
Abstract
Sports and Exercise Medicine (SEM) has rapidly evolved into a formally recognised and important medical specialty that supports population health and tackles chronic disease burden in addition to injury prevention and optimisation of athletic performance. The specialty's establishment has adopted different timelines globally, and training pathways differ markedly across English-speaking countries, despite common clinical goals. In this review, we aim to provide a comparison between the postgraduate training pathways for physicians in SEM across five English-speaking countries highlighting the main differences, strengths and drawbacks of each pathway. This review will be able to guide future changes in the training pathway and inform aspiring trainees considering a career in SEM. Data on pathways to board certification in SEM, training program requirements, structure, duration, examinations and competition levels was collected from literature, official governing bodies' publicly available documents and online resources. Postgraduate training pathways in SEM vary internationally in structure, duration and content. The UK, Australia and New Zealand recognise SEM as a stand-alone specialty delivered through nationally standardised programs, providing sustained exposure across musculoskeletal, exercise and wider population health domains. The US and Canada offer SEM as a subspecialty via shorter fellowships delivering procedural focus and increased team medicine involvement but with greater variability in content and reduced emphasis on exercise medicine. Structured programs ensure curricular consistency and depth but require longer training and face high competition for posts. Fellowship models enable faster entry to independent practice and maintenance of dual specialty roles at the risk of narrowing clinical focus. Recognising the strengths and drawbacks of each pathway can inform refinement of SEM training internationally and guide aspiring SEM physicians in selecting pathways aligned with their career goals and the demands of both training and application processes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".