P.013 Needs assessment of rural telemedicine care for Parkinson disease in British Columbia
Bibliographic record
Abstract
Background: People with Parkinson disease (PD) face progressive mobility loss, but medical treatment is dependent on clinical assessment and examination. Regional patient and physician density patterns pose further problems to accessing care. Telehealth may improve access particularly among rural populations, but an approach to this problem should consider patient perspectives. Methods: We surveyed and conducted a focus group for people with PD and their caregivers. Questions assessed perceptions of barriers to neurological care and use of telehealth for PD management. Thematic analysis was performed to classify qualitative data. Results: 18 individuals completed the survey and 7 parties joined the focus group. 52% of participants travel >50km for neurologist appointments (range = 59 to 842km). Perceived barriers include cost and difficulty of travel, wait times, lack of interdisciplinary healthcare and deep brain stimulation outside large cities. 80% of participants (95% C.I. 64-96%) would likely or very likely use telehealth for follow-up neurologist appointments if proven as good as in-office visits. Participants associated telehealth with improved quality of care, improved access to care, and cost savings. Conclusions: This sample of people with PD and their caregivers report willingness to access care via telehealth to reduce perceived cost and travel for specialty care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".