Ballet Pointe Shoe Discomfort: An Exploration Through the use of Real-Time Assessments while Performing en Pointe Movements
Bibliographic record
Abstract
Despite the advancement in technology, the ballet pointe shoe remains largely unchanged and few research studies have investigated it.The pointe shoe is still in use today and poses many disadvantages to the ballet dancer's foot health.This research aims to uncover ballet dancers' feet discomfort while dancing en pointe, in the hopes of contributing to the improvement of the future design of pointe shoes.This research brought together a human-centred approach in order to comprehend and gain different perspectives on the topic in a real-time setting.Real-time assessments were obtained using questionnaires, a 3-D apparatus of feet en pointe and thermographic imaging in a simulated ballet laboratory.Ten ballet participants currently practicing pointe work with at least two years of pointe work experience took part in the study.During the study, participants completed a pre-test questionnaire, three pointe work movements, followed by real-time discomfort assessments, thermographic imaging sessions and a post-test questionnaire.Through data triangulation, the great toe area was found to be the most frequently assessed by participants with the highest average discomfort intensity and average temperature variation.Increased temperature areas found to be a predictor for discomfort intensity, in complex movements like bourrée.Results demonstrated the relationship of the various methods used and their contribution on examining real-time pointe shoe discomfort and pointe shoe design recommendations. Keywords: human-centered
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".