Willingness to participate in clinical trials: A cross-sectional analysis in Ho Chi Minh City, Vietnam
Bibliographic record
Abstract
Context: Clinical trial is an experiment on comparable groups of human beings which evaluates the efficacy of a treatment or medical intervention by comparing the effects with other testing treatments or control treatments. Clinical trials help develop alternative treatment solutions or a preventative method for disease as well as support participants with medical and healthcare services during the trial period. Other benefits of clinical trials include gaining information, ensuring health issues, detecting early disease symptoms, and reducing medical services costs. Aims: To evaluate willingness to participate (WTP) in clinical trials (CT) in Ho Chi Minh city and examine the factors associated with it. Methods: A cross-sectional study was conducted through the online sampling method and gathered 581 valid responses during two weeks in February 2022. Results: Among 581 respondents, 71.6% stated they were willing to participate in CT, while 48.2% stated that they would let their family enroll. WTP in CT was higher in participants of younger age, single, not having children, healthy, and without chronic disease. Vietnamese age was associated with WTP in CT. Conclusions: Assessing WTP in CT helps state management agencies, organizations and research institutes design suitable CT models and increase the voluntary participation rate in CT.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".