Impact of the COVID-19 Pandemic on People Living with Rheumatoid Arthritis: Experiences and Preferences in Accessing Healthcare Across Five Countries
Bibliographic record
Abstract
INTRODUCTION: The global coronavirus 2019 (COVID-19) pandemic created many challenges in healthcare provision. This study aimed to evaluate the global impact of the COVID-19 pandemic on people living with rheumatoid arthritis (RA). METHODS: The RA Narrative COVID-19 survey was conducted online among people with RA who resided in Brazil, Canada, France, Japan, and the US from August to September 2021. The survey examined disease management, healthcare access and experiences, and participant preferences for interactions with their doctor. RESULTS: Overall, 500 participants completed the survey: 100 each resided in Brazil, Canada, France, Japan, and the US. Emotional well-being was the aspect of disease management most reported to be negatively impacted by the pandemic (55% of participants); 'having more anxiety and/or stress' during the pandemic was the top factor that made controlling RA symptoms more difficult (49% of participants). In comparison, the top factor that made controlling RA symptoms easier was 'having a less busy schedule' (35% of participants). More participants had virtual appointments during versus pre-pandemic (53% vs. 13%, respectively) and participants were equally satisfied with the overall quality of care received via virtual and in-person appointments (76% of participants were 'satisfied' or 'very satisfied' with both). However, participants generally preferred in-person over virtual appointments, except for prescription refills, for which preferences were similar (39% vs. 36%, respectively). CONCLUSIONS: This survey suggests that the COVID-19 pandemic did negatively impact some aspects of disease management for people living with RA but had positive impacts on the utilization of virtual care. Although participants generally preferred in-person appointments, these results position virtual care as an appropriate means for routine follow-ups.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".