Assessing Performing Artists in Medical and Health Practice — The Dancers, Instrumentalists, Vocalists, and Actors Screening Protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it