Piece of Mind: knowledge translation performances for public engagement on Parkinson’s disease and dementia
Bibliographic record
Abstract
Introduction The subjective experience of illness is often overshadowed by the disease-and-cure focus of health research, contributing to the stigmatization of conditions such as Parkinson’s disease and dementia. This is exacerbated by the fact that traditional means of knowledge dissemination are inaccessible to non-academic audiences, hampering meaningful dialogue with and research uptake by the broader community. Methods Our arts-based knowledge translation project, Piece of Mind, brought together neuroscientists, people with Parkinson’s disease or dementia, care partners and artists (musicians, dancers, circus acrobats) to co-create 2 multi-media performances based on scientific research and lived experience. We investigated whether the resulting interdisciplinary, multimedia performances could (1) challenge misperceptions around Parkinson’s/dementia; and (2) render neuroscientific research accessible to a diverse audience. Prior to and immediately following virtual screenings of the feature-length Piece of Mind Parkinson’s and Dementia filmed performances, audience members were invited to complete pre-post questionnaires comprised of demographic, Likert-scale and open-ended questions. Results Responses indicated that both performances elicited strong emotional engagement and improved self-reported understanding and empathy towards individuals with Parkinson’s and dementia. Based on a thematic analysis on open-ended questions, we consider the barriers and facilitators to the audience’s receptiveness and discuss the performances’ potential as a knowledge translation tool. Discussion By presenting an emotionally engaging perspective on Parkinson’s and dementia, Piece of Mind acts as an important complement to text-based knowledge dissemination in health research.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".