Intention to participate in COVID-19 vaccine clinical trials in May 2021: a cross-sectional survey in the general French population
Bibliographic record
Abstract
In May 2021, while the immunization campaign was in progress, the emergence of new SARS-CoV-2 variants led us to assess attitudes toward participation in a COVID-19 vaccine clinical trial (VCT) in France. Between the 10th and the 23rd of May 2021, we conducted a cross-sectional online survey among a representative sample of the French population aged 18 and over and a specific sample of the French population over 65. Among the 3,056 respondents, 28.0% (856) would consider participation in a COVID-19 VCT. Factors independently negatively associated with willingness to participate in a COVID-19 VCT were female gender with an adjusted odd ratio (aOR) 0.42 and 95% confidence interval (95% CI) 0.35-0.51, and mistrust in health actors (in their own physician and pharmacists, health ministry, government, scientists in medias, medias and pharmaceutical companies) with aOR 0.86 (95% CI 0.84-0.88) by one-point increase in mistrust in health actors score. Factors positively associated with willingness to participate in a COVID-19 VCT were COVID-19 vaccination or intention to get vaccinated with aOR 4.89 (95% CI 3.15-7.61), being a healthcare worker with aOR 2.051 (95% CI 1.51-2.80), being at risk for severe COVID-19 with aOR 1.39 (95% CI 1.14-1.69) and altruism as the main reason for getting vaccination with aOR 1.56 (95% CI 1.29-1.88). In May 2021, despite COVID 19 vaccine availability, 28% of the French population would agree to participate in a COVID-19 VCT. Mistrust in health actors contributes to a reduction in the intention to participate. Attitudes toward COVID-19 vaccination predict attitudes toward participation in a COVID-19 VCT.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".