Canadian Healthcare Access in Parkinson’s Disease and COVID-19: A Cross-Sectional Survey
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a common chronic neurodegenerative condition. As a result of the COVID-19 pandemic, healthcare provision faced challenges worldwide. We aimed to explore how the COVID-19 pandemic changed healthcare experiences for people living with Parkinson's disease (PwP) in Canada. METHODS: We conducted a national cross-sectional online survey about healthcare access for PwP in 2020. Participants (n = 298) were recruited through Parkinson Canada, the national patient association and its provincial partners, that advertised the study in a monthly newsletter. We used descriptive statistics and multivariate regression modelling to test associations of interest. A P < 0.05 was deemed statistically significant. RESULTS: During the COVID-19 pandemic, PwP reported greater difficulty obtaining PD-related healthcare services and lesser satisfaction with healthcare provision compared to pre-pandemic experiences. Dissatisfaction with care was associated with the presence of barriers to access services, a lack of confidence in accessing services remotely, pre-pandemic care dissatisfaction, and difficulty in obtaining care during the COVID-19 pandemic. Unmet care needs were associated with a lack of confidence in accessing services remotely, dissatisfaction with pre-pandemic care, difficulty obtaining pre-pandemic care, and communication challenges. CONCLUSION: Our results suggest that healthcare experiences for PwP significantly changed during the COVID-19 pandemic, with challenges in access to virtual care. Poorer pre-pandemic care experiences were amplified during the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".