Participant Perspectives Concerning Resuming Clinical Research in the Era of COVID-19
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic caused a shutdown of clinical research but offered a unique opportunity to understand attitudes and motivations around contributing to clinical research and resuming in-person visits during a pandemic. Methods: We conducted an anonymous survey study at Pennington Biomedical Research Center (PBRC) in participants returning for in-person visits from May 26, 2020 to August 11, 2020 and in people who previously expressed interest in research via an online Research Electronic Data Capture (REDCap) survey from August 6, 2020 to September 11, 2020. The survey gathered demographic information and presented statements that required answers on a scale of 1 (absolutely disagree) to 10 (absolutely agree). Two hundred fifty-one people completed paper surveys in-person while 1,537 people completed the survey online. Results: Online participants were more likely to be female (75.2% vs. 56.8%), more likely to have had COVID-19 symptoms (19.6% vs. 5.2%), and more likely to know someone with COVID-19 (72.7% vs. 49.4%). More people who came in-person thought they were low risk for severe COVID-19 compared to those who filled out the survey online (52.2% vs. 38.4%, P = 0.0002). More people who completed the survey online preferred to do as many study visits over the phone or internet as possible (37.8% vs. 22.7%, P < 0.0001). More people who came in-person agreed that clinical research is even more important than before COVID-19 (54.2% vs. 44.3%, P = 0.0035). Conclusions: The majority of people felt that clinical research is important because of the health benefits received and because it may help others. These data may provide important considerations in the planning of future studies in the era of COVID-19.
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 | 0.037 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".