‘Find your groove’: exploring how dancing can support physical literacy for individuals with Parkinson’s
Bibliographic record
Abstract
The purpose of this study was to examine teaching strategies utilized by instructors within a dance class for individuals with Parkinson’s, and to describe ways in which dancers respond to the teaching strategies utilized; both through the lens of physical literacy. Dance instructors offer important insight into the content design and facilitation of these classes for individuals with Parkinson’s so they can experience the physical, psychological, and social benefits of dancing. Observations of nine dance classes (occurring online and via hybrid format) were conducted. Data was analyzed using reflexive thematic analysis and five themes were created: (1) Tuning in and connecting dancers with their bodies; (2) Creating a fun, low-pressure, and responsive environment; (3) Designing opportunities for dancers to be creative during class; (4) Overcoming challenges and feeling successful; (5) Connecting and dancing together. Dance classes led by adaptable, encouraging, and responsive instructors offer joyful and motivating dancing experiences for individuals with Parkinson’s. These findings paint a picture of specific pedagogical strategies and behaviours utilized by dance instructors to support expressions of physical literacy within classes for individuals with Parkinson’s.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".