Effects of the COVID-19 Pandemic on Parkinson’s Disease: a Single-Centered Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The public health measure restrictions across the world due to COVID-19 have inadvertently impacted the routines for people with Parkinson's disease (PD) and their care partners not only in terms of compromised neurological clinical care but also drastically changing the way of life to minimize the risk of becoming infected. This study explores initial PD patients' lived experiences to observe how quality of life and health care has been affected at the start of the COVID-19 pandemic and provide insight into the importance of patient engagement and virtual care. METHODS: Twenty-two virtual, in-depth semi-structured interviews with persons diagnosed with PD who usually attend a Movement Disorders specialty clinic in Calgary, Alberta, were completed between April 28 and May 13, 2020, and the care partners that wished to participate. Interviews were recorded and transcribed, after which transcripts were analyzed and coded into relevant themes using NVivo 12. RESULTS: Impacts from the public health measures and COVID-19 results into three main themes: (1) Impacts of COVID-19 on PD Clinical Care; (2) Activities of Daily Living; (3) Attitudes and Perceptions. Participants reported worsening in motor and nonmotor symptoms and had to accommodate to clinical care via virtual means which were associated with limitations and suggestions for improvement of remote care. CONCLUSION: This study provides a unique opportunity for researchers to better understand the lived experiences of PD patients in all aspects of their life suggesting that innovative means are needed for facilitating virtual health care medicine and increased social interaction.
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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 | 0.012 | 0.016 |
| 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 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".