Building a Learning System Guided by Client Stories and Evaluation: Dancing with Parkinson’s Stories That Illuminate Pathways to Better Brain Health
Bibliographic record
Abstract
This chapter explores how Dancing with Parkinson’s (DWP), a research-informed dance program for individuals with Parkinson’s disease and older adults, evolved into a learning organization by integrating client stories, daily feedback, and external evaluations. Initially offering in-person classes, DWP transitioned to a daily online Zoom platform during the COVID-19 pandemic, expanding access to over 5300 participants across Canada. The program employs visualization and mirroring techniques to enhance neuroplasticity, mobility, and social connection, supported by evidence of improved balance, energy levels, and reduced isolation. Daily pre- and post-class chats provided real-time insights into participants’ health, preferences, and barriers, informing program adaptations like music selection and schedule adjustments. External evaluations revealed 85% of participants found the classes gave them “something to look forward to,” while 78% reported increased energy. DWP’s learning system emphasizes an “ecology of evidence,” blending quantitative data with qualitative stories to refine outreach and honor diverse community needs. Partnerships with institutions like the University of Hawaiʻi enriched the evaluation capacities of the DWP programming leads. By centering participant voices and maintaining flexibility across online/in-person formats, DWP models how community-driven interventions can foster equitable brain health through creativity, cultural responsiveness, and sustained relational learning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".