Building a Learning System Guided by Client Stories and Evaluation: Dancing with Parkinson’s Stories That Illuminate Pathways to Better Brain Health
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it