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.
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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 | 0.127 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".