Covid-19 research evidence: An international survey exploring views on useful sources, preferred formats, and accessibility
Bibliographic record
Abstract
INTRODUCTION: In a pandemic, stakeholders such as policy makers, clinicians, patients, and the public need access to high-quality, timely, relevant research evidence in a format that is understandable and applicable. OBJECTIVES: An online survey was used to determine where a global audience finds research evidence about COVID-19 and how they prefer to keep up to date. METHODS AND MATERIALS: We conducted an online survey of people interested in research in English and Spanish. We used a convenience sample of people visiting websites and social media accounts of Cochrane, an international organisation that collates systematic reviews of research. RESULTS: 831 people with various roles and locations responded over a short period with little active promotion. Healthcare professionals, members of the public, and policy influencers wanted research evidence to inform decisions about COVID-19. More than half found research evidence from government websites (52%), international organisations (57%), journals (56%), and evidence collation organisations (60%) useful. People wanted research evidence about COVID-19 formats such as lay summaries (60%), online systematic reviews (60%), short summaries with commentaries (51%), and visual summaries (48%). People preferred to be kept up to date about COVID-19 research via email updates and newsletters, tailored to people’s interests (34%), traditional media (13%) and social media (12%). CONCLUSIONS: It was feasible to collect feedback rapidly using a simple online survey. Websites from official organisations were key sources of COVID-19 research evidence. More research is needed on how best to provide evidence that is easy to access and understand.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".