Delphi developed syllabus for the medical specialty of sport and exercise medicine: part 2
Bibliographic record
Abstract
Training in the medical specialty of sport and exercise medicine (SEM) is available in many, but not all countries. In 2015, an independent Delphi group, the International Syllabus in Sport and Exercise Medicine Group (ISSEMG), was formed to create a basic syllabus for this medical specialty. The group provided the first part of this syllabus, by identifying 11 domains and a total of 80 general learning areas for the specialty, in December 2017. The next step in this process, and the aim of this paper was to determine the specific learning areas for each of the 80 general learning areas. A group of 26 physicians with a range of primary medical specialty qualifications including, Sport and Exercise Medicine, Family Medicine, Internal Medicine, Cardiology, Rheumatology and Anaesthetics were invited to participate in a multiple round online Delphi study to develop specific learning areas for each of the previously published general learning areas. All invitees have extensive clinical experience in the broader sports medicine field, and in one or more components of sports medicine governance at national and/or international level. SEM, Family Medicine, Internal Medicine, Cardiology, Rheumatology and Anaesthetics were invited to participate in a multiple round online Delphi study to develop specific learning areas for each of the previously published general learning areas. All invitees have extensive clinical experience in the broader sports medicine field, and in one or more components of sports medicine governance at national and/or international level. The hierarchical syllabus developed by the ISSEMG provides a useful resource in the planning, development and delivery of specialist training programmes in the medical specialty of SEM.
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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 | 0.052 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".