Understanding the experience of prescription charges in people living with parkinson’s disease: a focus group study
Bibliographic record
Abstract
BACKGROUND: In England, people aged > 60 are typically required to pay for their prescriptions. Whilst exemption criteria enable people living with specified long-term health conditions to receive free prescriptions, Parkinson's disease is omitted from this list. People with Parkinson's are often reliant upon medications, and evidence suggests that medical fees can reduce quality of life and medicine adherence. We, therefore, aimed to explore the impact of prescription charges on people with Parkinson's and their family care partners (caregivers). METHODS: This is a qualitative focus group study with people with Parkinson's and caregivers. Focus groups were semi-structured and conducted online. Participants were recruited through opportunity sampling. Eligible participants were adults aged 18 and over living in England who either (1) had a diagnosis of Idiopathic Parkinson's Disease or (2) provided unpaid care for someone with Parkinson's, including parents, adult children, siblings, or close friends. Data was analysed using reflexive thematic analysis within a critical realist paradigm. RESULTS: Five focus groups were conducted with people with Parkinson's (n = 12) and caregivers (n = 12). All focus groups comprised both people with Parkinson's and caregivers. Thematic analysis identified three overarching themes: (1) The financial toll of medication and its ripple effects; (2) Lack of inclusion and support; and (3) Difficulties of seeking support. People affected by Parkinson's disagreed with current policy and suggestions of per-prescription charge re-evaluation were expressed. CONCLUSIONS: Prescription charges have multifaceted negative impacts on people affected by Parkinson's. Current prescription charge policies, including their exemption criteria, should be reviewed, alongside initiatives to raise awareness of existing financial support systems, such as pre-payment certificates. REGISTRATION: Study protocol and analysis strategy are pre-registered on Open Science Framework ( https://osf.io/y8ve5/ ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".