Assessing Performing Artists in Medical and Health Practice — The Dancers, Instrumentalists, Vocalists, and Actors Screening Protocol
Bibliographic record
Abstract
ABSTRACT: Training in the performing arts exposes individuals to often extreme physical and psychological demands, which are linked to high occupational injury rates. The intense demands of performing artists have been likened to those of sport athletes. However, distinct differences in these demands necessitate specialized approaches to the health care of performing artists. Through the Athletes and the Arts collaboration, the American College of Sports Medicine and Performing Arts Medicine Association identified that the creation of a specialized preparticipation screening tool for performing artists would likely enhance health care for performing artists significantly. Based on a thorough review of established assessments and an extensive consultation process with domain experts, a consensus best-practice screening tool was developed: the Dancer, Instrumentalist, Vocalist, Actor (DIVA) Preparticipation Screening. This screening tool is modeled on the athletic preparticipation examination (PPE) in its structure and 30-min target duration. However, DIVA diverges considerably from the PPE in its content to address the specific risks and needs of performing artists. In particular, screening questions and physical examination procedures focus strongly on musculoskeletal injuries and mental health conditions, in response to the preponderance and interactions of these conditions appearing in performing artists. The DIVA tool presented is intended as a "living tool," which can be modified in the future to include new effective assessment techniques as appropriate. Training in the DIVA preparticipation physical examination is included as a core component of the essentials of performing arts medicine continuing education course described in detail in a companion manuscript in this issue.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".