COVID-19: investing in country capacity to bridge science, policy and action
Bibliographic record
Abstract
The COVID-19 pandemic has put research evidence and its use in health policy-making under a new spotlight.1 Faced with the need for immediate action, as most recently shown with vaccination roll-out strategies,2 many politicians and other leaders have publicly stressed the need to follow the ‘science’. Scientific advisors and advisory bodies have gained unprecedented visibility. At the same time, the conflicts between researchers/health experts and political decision-makers have, now and then, been vividly brought to the fore.3 To bridge the divide, building and strengthening knowledge translation (WHO defines knowledge translation as the exchange, synthesis and effective communication of reliable and relevant research results. The focus is on promoting interaction among the producers and users of research, removing the barriers to research use, and tailoring information to different target audiences so that effective interventions are used more widely.) organisations, which act as institutional bridges between researchers and both decision-makers and communities, is called for.4 More than ever before, countries need to counter misinformation and rapidly mobilise the best available evidence, and present it in userfriendly ways to decision-makers.5 The WHO has been championing the need for research evidence to inform decision-making in the context of COVID-19. Although at times also challenged to provide clear guidance in …
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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 | 0.040 | 0.138 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.029 | 0.031 |
| Insufficient payload (model declined to judge) | 0.027 | 0.016 |
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".