Impact of the COVID-19 pandemic on perceived access and quality of care in German people with parkinsonism
Bibliographic record
Abstract
Due to the heterogeneous clinical presentation, people with Parkinsonism (PwP) develop individual healthcare needs as their disease progresses. However, because of limited health resources during the COVID-19 pandemic, many patients were put at risk of inadequate care. All this occurred in the context of inequitable healthcare provision within societies, especially for such vulnerable populations. This study aimed to investigate factors influencing satisfaction and unmet need for healthcare among PwP during the COVID-19 pandemic in Germany. Analyses relied on an anonymous online survey with a 49-item questionnaire. We aimed at describing access to health services before and during the early stages of the pandemic. To this end, a generalized linear model was used to derive significant predictors and a stepwise regression to subsummarize the main factors of perceived inadequate care. In total, 551 questionnaires showed that satisfaction with Parkinsonism-related care decreased significantly during the pandemic (p < 0.001). In particular, factors such as lower educational level, lower perceived expertise of healthcare providers, less confidence in remote care, difficulties in obtaining healthcare, and restricted access to care before the pandemic but also lower densities of neurologists at residence and less ability to overcome barriers were indicative of higher odds to perceive unmet needs (p < 0.05). The results unveil obstacles contributing to reduced access to healthcare during the COVID-19 pandemic for PwP. These findings enable considerations for improved provision of healthcare services to PwP.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".