386. Participant Reflections on Their Experiences Participating in Phase 1 and 2 Inpatient Vaccine Trials and Human Challenge Studies
Bibliographic record
Abstract
Abstract Background Volunteers are critical for the success of clinical trials but there is still debate on the ethics surrounding recruitment and enrollment of healthy human subjects who are paid for their participation in such research. This study seeks to examine the experiences and perceptions of risks and benefits of participants in early-phase inpatient studies to gain greater insight into how they feel about participating. Methods An interviewer-administered survey was conducted with 152 healthy volunteers who participated in at least one Phase 1 or 2 inpatient vaccine or challenge clinical trial at the Johns Hopkins Center for Immunization Research, during the years of 2010-2020. Participant characteristics, study experiences, and perceived risks were analyzed. Poisson regression with robust variance was used to examine factors associated with perception of risk before and after participation. Results Most participants who completed the survey were male (62%) and Black or African American (78%). 53% attained at most a high school degree and 42% had an income below $18,500 a year. Compensation was the primary factor for joining a study for 68% of participants; 35% of them relied on clinical trials as a major part of their income. Nevertheless, 32% reported they declined to participate in at least one study and 63% reported that they ask about the side effects before joining a new study. Over 90% of participants agreed or strongly agreed that they enjoyed participating, wanted to participate, and were glad they joined the study. There was a significant reduction in the perceived risk of study participation after study completion compared to perceived risk before joining the study, where before the study 46% thought it was “Not at all risky” compared to 73% afterwards. Conclusion Most participants in our early-phase inpatient studies are males of lower income and compensation was the primary motivator for joining. However, side effects do factor in their decision whether to join. The majority of participants indicated that they were glad they joined the study. Despite the studies having risks and expected side effects, there was also a significant decline in perception of risks related to the study following participation, which implies participants feel safer than they anticipated feeling. Disclosures Lawrence Moulton, PhD, Pfizer Canada ULC: Employee Kawsar R. Talaat, MD, Intralytix: Advisor/Consultant|Merck: Advisor/Consultant|NIAID: DSMB|Pfizer: Grant/Research Support|Pfizer: Pfizer contract with institution|Sanofi: Grant/Research Support|Takeda: Advisor/Consultant
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".