Moving Beyond Words: Co-designing Artistic and Technological Avenues for Communicating Parkinson’s Disease Experience
Bibliographic record
Abstract
What happens when the body’s messages cannot be conveyed in words? In Parkinson's Disease (PD), the discrepancy between lived and communicated experience creates relational gaps that impact social connection and well-being of people with Parkinson’s disease and their caregivers. This pilot study aimed to characterize the communication needs of Parkinson’s disease stakeholders, and to co-design non-verbal strategies for bridging experiential gaps. Interviews with people with Parkinson’s, informal care partners, and healthcare providers revealed complex challenges arising from stigma, relational factors, and intertwined motor, affective and cognitive aspects of disease progression. Participants highlighted the limitations of verbal communication across multiple contexts and described dream solutions to convey PD experiences in a multi-modal manner. Two non-verbal approaches were subsequently explored: 1) workshopping the SymPulseTM armband, an existing technology that simulates parkinsonian tremor; and 2) collaboration with a visual artist to develop “Envisagez l’invisible”, a portraiture activity that prompts reflection on the invisible aspects of PD. We detail the participatory process, report on preliminary user feedback, and share emerging challenges, reflections and strategies to guide future co-design.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".