How to persevere in a ballet performance career: exploring personal wisdom of retired professional ballet dancers
Bibliographic record
Abstract
Despite the well-documented challenges of professional ballet dancers and anecdotal report of premature retirement, no research has examined what professional ballet dancers do to persevere in their performance careers. This study drew on the perspectives of retired professional ballet dancers to address this question. We conducted narrative interviews with nine retired professional ballet dancers and identified four themes using thematic analysis. Findings show that to persevere in a performance career, dancers need to: (1) Look After Mental Health and Self-Worth; (2) Be Proactive in Navigating Career; (3) Grow as an Artist; and (4) Live as a Whole Person. We suggest these thematic findings illustrate attitudes and behaviours of career adaptability and draw attention to the needs for mental health support and mental skills training, and mentorship and career advice, for helping aspiring and professional ballet dancers persevere in a performance career. Findings have implications and applications for ballet dancers, research, ballet management, dance educators, and dance counsellors.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".