An Intensive Continuing Education Course to Enhance Care of the Performing Arts Athlete: The “Essentials of Performing Arts Medicine”
Bibliographic record
Abstract
Abstract A new continuing education course from Performing Arts Medicine Association and American College of Sports Medicine enables sports medical health professionals to better care for performing artists. High occupational injury rates have been reported in performing artists, yet the quality of preventive and clinical care remains highly variable. Through the Athletes and the Arts collaboration, The Performing Arts Medicine Association, and the American College of Sports Medicine identified that health care practitioners’ existing expertise should be enhanced to address the complex psychophysical needs of performing artists. In response, a 2-d continuing education course, “The Essentials of Performing Arts Medicine” (EOPAM), was developed and has been delivered at least annually since 2016. This course has been well-received by 149 physicians and 240 allied health professionals to date (average ratings, ≥3.5/5 from 2018 to present), with course quality significantly improved by a transition to online delivery in 2020 (average ratings ≥4.5/5; P < 0.01). Accordingly, EOPAM demonstrates that a brief continuing education course can enhance health professionals’ understanding of the unique needs and demands of performing artists, addressing a key barrier to improved care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".