Perspectives of Physical Therapists regarding the use and Value of Screening Assessments and Preventative Programs for Elite-Level Dancers
Bibliographic record
Abstract
Inconsistency exists in research findings regarding the use and efficacy of screening assessments for elite-level dancers. The purpose of this study was to gain an understanding of physical therapists' perspectives on the use and value of screening assessments and preventative measures in this population in order to inform future research and clinical care. Semi-structured interviews were conducted with nine physical therapists with a caseload of at least 25% dancers aged ≥ 14 years and enrolled in a professional or pre-professional program (i. e., elite- level dancers). Transcribed interviews were analyzed using an Interpretive Description framework. A constant comparative analysis was used to identify similarities and differences among and within the data. Themes and categories were finalized after consensus among research team members. Resulting themes included the values, challenges, barriers, and opportunities of screening and preventative programs. Values extended beyond injury prevention to such matters as performance optimization, development of rapport, communication, and education. Challenges included a lack of research and support from dancers, artistic staff, and the traditional dance culture. Opportunities were seen for improved standardization and innovation of screening procedures. Recommendations to guide clinical practice and research represent an initial step toward improving implementation of screening and preventative programs, which may provide benefits to dancers' health and performance.
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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 | 0.069 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".