Exploring Dance Movement Therapy as Quality Physical Activity for People with Parkinson's Disease
Bibliographic record
Abstract
This project explores the quality of physical activity people with Parkinsons Disease (PD) experience via a case study of a specialized Dance Movement Therapy (DMT) program, Dancing with Parkinsons (DwP), based in Toronto, Ontario, Canada. The Quality Participation (QP) model developed by Martin Ginis and colleagues (2016) served as the framework by which participants quality participation experiences in the DwP program, including the meanings and satisfactions they derived from participating in the program, were investigated. The objectives of this study were twofold: to explore the quality experiences of PD patients participating in the DwP program, and to better understand participants experiences in DMT through the lens of the QP model. Drawing on qualitative research methods, semi-structured interviews were conducted with eight participants (two male and six female) from the DwP program. Thematic analysis of the data highlighted three key conditions that enabled the participation in the DwP program (physical environment, activity and social environment), and foregrounded three of the six themes defined in the QP model (autonomy, engagement and belongingness). This project aimed to contribute to research on DMT and to quality physical activity participation in general, with focused attention on participation among populations with PD specifically.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".