Living evidence and adaptive policy: perfect partners?
Bibliographic record
Abstract
BACKGROUND: While there has been widespread global acceptance of the importance of evidence-informed policy, many opportunities to inform health policy with research are missed, often because of a mismatch between when and where reliable evidence is needed, and when and where it is available. 'Living evidence' is an approach where systematic evidence syntheses (e.g. living reviews, living guidelines, living policy briefs, etc.) are continually updated to incorporate new relevant evidence as it becomes available. Living evidence approaches have the potential to overcome a major barrier to evidence-informed policy, making up-to-date systematic summaries of policy-relevant research available at any time that policy-makers need them. These approaches are likely to be particularly beneficial given increasing calls for policy that is responsive, and rapidly adaptive to changes in the policy context. We describe the opportunities presented by living evidence for evidence-informed policy-making and highlight areas for further exploration. DISCUSSION: There are several elements of living approaches to evidence synthesis that might support increased and improved use of evidence to inform policy. Reviews are explicitly prioritised to be 'living' by partnerships between policy-makers and researchers based on relevance to decision-making, as well as uncertainty of existing evidence, and likelihood that new evidence will arise. The ongoing nature of the work means evidence synthesis teams can be dynamic and engage with policy-makers in a variety of ways over time; and synthesis topics, questions and methods can be adapted as policy interests or contextual factors shift. Policy-makers can sign-up to be notified when relevant new evidence is found, and can be confident that living syntheses are up-to-date and contain all research whenever they access them. The always up-to-date nature of living evidence syntheses means producers can rapidly demonstrate availability of relevant, reliable evidence when it is needed, addressing a frequently cited barrier to evidence-informed policymaking. CONCLUSIONS: While there are challenges to be overcome, living evidence provides opportunities to enable policy-makers to access up-to-date evidence whenever they need it and also enable researchers to respond to the issues of the day with up-to-date research; and update policy-makers on changes in the evidence base as they arise. It also provides an opportunity to build flexible partnerships between researchers and policy-makers to ensure that evidence syntheses reflect the changing needs of policy-makers.
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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 | 0.344 | 0.492 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.066 |
| Scholarly communication | 0.041 | 0.088 |
| Open science | 0.009 | 0.049 |
| Research integrity | 0.032 | 0.038 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".