French Public Familiarity and Attitudes toward Clinical Research during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic put clinical research in the media spotlight globally. This article proposes a first measure of familiarity with and attitude toward clinical research in France. Drawing from the "Health Literacy Survey 2019" (HLS19) conducted online between 27 May and 5 June 2020 on a sample of the French adult population (N = 1003), we show that a significant proportion of the French population claimed some familiarity with clinical trials (64.8%) and had positive attitudes (72%) toward them. One of the important findings of this study is that positive attitudes toward clinical research exist side by side with a strong distancing from the pharmaceutical industry. While respondents acknowledged that the pharmaceutical industry plays an important role in clinical research (68.3%), only one-quarter indicated that they trust the industry (25.7%). Positive attitudes toward clinical trials were associated with familiarity with clinical trials (Odds Ratio, OR 2.97 [1.90-4.63]), financial difficulties (OR 0.63 [0.46-0.85]), as well as mistrust of doctors (0.48 [0.27-0.85]) and of scientists (OR 0.62 [0.38-0.99]). Although the French media provided a great deal of information on how clinical research works during the first months of the pandemic, there remains profound mistrust of the pharmaceutical industry in France. This suspicion can undermine crisis management, especially in the areas of vaccine development and preparation for future pandemics.
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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".