Developing a dancer wellness program employing developmental evaluation
Bibliographic record
Abstract
Wellness programs are being increasingly employed with performing artists. Given their aim of reducing injuries, injury tracking is commonly employed as an outcome measure. Evaluating the development and process of a wellness program can also enhance its effectiveness. Developmental evaluation offers one methodological framework within which to conduct such investigations. This paper reports on a 2-year process involving feedback from professional ballet dancers, management and artistic staff, and healthcare providers at a ballet company in order to develop a dancer screening and wellness program. Following a consultation phase, an initial program composed of an expanded medical team and annual injury prevention screen was proposed. Alongside implementation with 30 professional ballet dancers, formal and informal feedback was sought from stakeholders and members across all levels of the ballet company to facilitate ongoing development, evaluation, and revision of the wellness program. The use of a process informed by developmental evaluation helped identify strengths and limitations within the screening process. The collective expertise of the assessors was used to modify the components and process of the screen to strive for ecological appropriateness. The process also fostered buy-in from all involved. Participant feedback helped refine the medical team available to the dancers and influenced the treatment and referral pathways via which dancers are able to access each member of the medical team. Furthermore, reflective discussions with artistic and management staff brought to light potential interactions between repertoire programming, fitness, and injury patterns. This prompted a reconsideration of how artists are trained and supported. Evaluation methods that focus on experiences and insight gained during program development stand to result in more efficient screening programs and health-promotion models and, ultimately, healthier performing artists.
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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 | 0.046 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".