Why do healthy volunteers participate in clinical trials?
Bibliographic record
Abstract
Aim: To better understand the motivations and needs of healthy volunteers in clinical trials. Methods: The European Federation for Exploratory Medicines Development (EUFEMED) devised a survey of 58 questions on motivations of volunteers to participate, as well as on needs at the level of convenience, risk/benefit, social interaction, partnership, and altruism. The survey was shared with European Clinical Research Units (CRUs) who agreed to conduct the survey in volunteers taking part in their phase 1 trials. Results: The survey generated responses from 4349 healthy volunteers. In 54% of cases contributing to new treatments was the primary motivation to participate, versus monetary compensation in 41% of responders. Participant requirements during overnight stays defined as important were well-functioning showers (89%), ability to spend some time outside (78%), the meal quality (76%), access to high-speed internet (74%), a place to read/work alone (69%), and a good night rest (69%). Male participants were less focused on a potential health risk than females. The opportunity to socialize with staff was important for 85%, a unit with a cozy interior for 70%, and socializing with other participants for 63% of volunteers. Almost all (91%) volunteers expressed an interest in receiving information on the findings of the trial in lay language. Conclusion: Healthy volunteers in clinical trials have specific needs during their stay in the Clinical Research Unit. Volunteer recruitment, compliance and retainment, as well as an ethical and a subject-centric approach to trial design and conduct can benefit from understanding these needs.
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 | 0.100 | 0.320 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".