Qualitative evaluation to improve participant engagement and retention in remote COVID-19 treatment trials
Bibliographic record
Abstract
Abstract Clinical trials, although recognized as the gold standard for scientific research, encounter various challenges with participant recruitment and retention. Yet, given the surge in global research endeavors during the COVID-19 pandemic, mass recruitment and retention were needed to ensure study power. While participant engagement in clinical research has been investigated, there is limited literature underscoring motivations for participation in COVID-19 clinical trials conducted remotely. To address this gap, we conducted a longitudinal, qualitative evaluation study of participant engagement and retention in a remote, COVID-19 clinical trial. In-depth, semi-structured interviews were conducted between December 2020-March 2021 (timepoint 1; n = 19) and January–February 2022 (timepoint 2; n = 11) to gain insights from participants regarding their experiences, motivations, and challenges with clinical trial engagement during the COVID-19 pandemic. Findings identified several factors described to increase participant engagement, including perceived contribution to the greater good, protecting oneself and others, and gaining improved access to healthcare. Further, key themes underscoring the enhancement of retention, such as perceived trial-related challenges and recommendations for future studies, were also identified. Findings underscore the utility of embedding qualitative methods within clinical trials, and how they contribute to a broader understanding of factors influencing participation in COVID-19 trials while providing valuable insights to enhancing recruitment and retention strategies. Consequently, this study provides essential knowledge aimed at optimizing participant engagement and retention in clinical trials, thereby advancing our comprehension of and response to research mobilization during unprecedented events.
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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 | 0.230 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".