Parkinson's Beats at the World Parkinson's Congress in Barcelona. [Podcast]
Bibliographic record
Abstract
In this podcast episode, you can hear Helga Macfarlane chat to Jo Holland and Alison Williams, who have been involved with the Parkinson's Beats programme. As Julie Jones explains, it is widely accepted that exercise is beneficial for people living with Parkinson's, but finding a form of exercise that you enjoy and can do regularly can be challenging. Parkinson's Beats is a form of cardio drumming, which has been adapted to meet the needs of people with Parkinson's. Jo Holland attended a local cardio drumming class and fell in love with this form of exercise. Since then, she has been successful at securing funding from Parkinson's UK and the National lottery to set up the delivery of Parkinson's Beats, and now has classes twice a week, either face to face or online. The online class has people attending from all over Scotland, the wider UK and even overseas. Parkinson's Beats can be done sitting or standing, and incorporates aerobic exercise, large amplitude movements, auditory cues as well as cognitive or thinking challenge. Anecdotally, attendees reported physical and psychological and emotional benefits. Jo has since teamed up with other people with Parkinson's and researchers to evaluate the impact of Parkinson's Beats among those who participate regularly in the programme. The evaluation was funded by a small grant from Parkinson's UK, and the recent findings were presented at the World Parkinson's Congress by Jo Holland and Julie Jones.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.408 | 0.182 |
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".