Sustaining Health in Professional Ballet: Insights into Autonomy, Shared Expertise and Open Communication
Bibliographic record
Abstract
Objectives: To explore the perceptions of dancers and the supporting staff regarding the management of dancers’ health in a professional ballet company. Methods: Twenty-two dancers, health team members, artistic staff and administrators were interviewed, focusing on what is a healthy dancer, as well as the challenges and facilitators to prevent and manage health within the company. Analysis was conducted using principles of Grounded Theory. Results : Participants mentioned that being a healthy dancer was based on three main concepts: (1) achieving a dynamic balance of load through self-implemented strategies, (2) receiving support from their team and (3) navigating the aspects inherent to the professional ballet context. Dancers had to maintain a dynamic balance where they would adapt their load according to a constant assessment of their state (ie, pain, fatigue) and situations (ie, casting, opportunities, career). This dynamic balance was impacted by the support dancers receive from their entourage. They needed to establish relationships built on trust to ensure efficient communication and collaboration. Once established, the dancers’ entourage contributed to their assessment and the load adaptation strategies. The assessment and adaptation of load by dancers and the support provided were also influenced by contextual elements of ballet culture, including time and financial resources. Conclusion : To provide comprehensive care for dancers and maintain a dynamic balance, it is essential to empower dancers in their self-strategies through education and creating a positive work environment where open communication is encouraged.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".