Decentralized study of COVID Vaccine Antibody Response (STOPCoV): Results of a participant satisfaction survey
Bibliographic record
Abstract
The Covid-19 pandemic required many clinical trials to adopt a decentralized framework to continue research activities during lock down restrictions. The STOPCoV study was designed to assess the safety and efficacy of Covid-19 vaccines in those aged 70 and above compared to those aged 30-50 years of age. In this sub-study we aimed to determine participant satisfaction for the decentralized processes, accessing the study website and collecting and submitting study specimens. The satisfaction survey was based on a Likert scale developed by a team of three investigators. Overall, there were 42 questions for respondents to answer. The invitation to participate with a link to the survey was emailed to 1253 active participants near the mid-way point of the main STOPCoV trial (April 2022). The results were collated and answers were compared between the two age cohorts. Overall, 70% (83% older, 54% younger cohort, no difference by sex) responded to the survey. The overall feedback was positive with over 90% of respondents answering that the website was easy to use. Despite the age gap, both the older cohort and younger cohort reported ease of performing study activities through a personal electronic device. Only 30% of the participants had previously participated in a clinical trial, however over 90% agreed that they would be willing to participate in future clinical research. Some difficulties were noted in refreshing the browser whenever updates to the website were made. The feedback attained will be used to improve current processes and procedures of the STOPCoV trial as well as share learning experiences to inform future fully decentralized research studies.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".