Sustaining Health in Professional Ballet: Insights into Autonomy, Shared Expertise and Open Communication
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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