Communication between Medical Practitioners and Dancers
Bibliographic record
Abstract
The purpose of this study was to investigate patterns of communication between professional and pre-professional dancers and medical practitioners. One survey was developed and randomly conducted among family physicians, sports medicine physicians, chiropractors, physical therapists, and registered massage therapists. A second survey involved volunteer ballet and modern dancers in professional dance training programs, college and university dance programs, and independent dance artists. One hundred and ninety questionnaires were distributed to medical practitioners, and 50 were returned. Of 380 questionnaires given to dancers, 202 were returned. The dancers were 18 to 49 years old, with a majority between the ages of 18 and 20. They averaged more than 10 years of dance training. All of the questionnaires were distributed in a single large Canadian city. The data shows that medical practitioners rarely communicated with each other concerning a common (dance) patient. They also failed to communicate, in most cases, with the dancers' teachers, choreographers, and directors. This was not disconcerting to injured dancers, who tended to believe that such communication was not important to their recovery. Significantly, dancers did not fully understand the nature of their injuries when they sought medical advice, and they did not press the medical practitioners for additional information. Both groups generally believed that dancers would benefit by learning more about human anatomy.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".