Acceptability of Research and Health Care Visits During the COVID-19 Pandemic: Cross-sectional Survey Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has had a widespread impact on attendance in biomedical research and health care visits. OBJECTIVE: This study aimed to identify when and how American adults might feel comfortable about resuming in-person research and health care visits. METHODS: Cross-sectional questionnaire data were collected from 135 adults (age: median 48 years; women: n=113, 83.7%; White participants: n=92, 68.2%) who were engaged in health-related research. RESULTS: More than half of the respondents (65/122, 53.3%) felt that the COVID-19 pandemic positively affected their desire to participate in research. Although 73.6% (95/129) of respondents also indicated a willingness to attend in-person health care visits while Centers for Disease Control and Prevention (CDC) guidelines are implemented, 85.8% (109/127) indicated a willingness to attend in-person, outdoor visits, and 92.2% (118/128) reported a willingness to attend drive-through visits (with CDC guidelines implemented during both visit types). Videoconferencing was the most preferred format for intervention visits; however, adults over the age of 65 years preferred this format less than younger adults (P=.001). CONCLUSIONS: Researchers and clinicians should continue to provide opportunities for continuing the conduction of remote-based interventions while enforcing CDC guidelines during in-person visits.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".