Do evidence summaries increase policy-makers’ use of evidence from systematic reviews: A systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are important for decision-makers. They offer many potential benefits but are often written in technical language, are too long, and do not contain contextual details which makes them hard to use for decision-making. There are many organizations that develop and disseminate derivative products, such as evidence summaries, from systematic reviews for different populations or subsets of decision-makers. This systematic review will assess the effectiveness of systematic review summaries on increasing policymakers' use of systematic review evidence and to identify the components or features of these summaries that are most effective. METHODS/DESIGN: We will include studies of policy-makers at all levels as well as health-system managers. We will include studies examining any type of "evidence summary," "policy brief," or other products derived from systematic reviews that present evidence in a summarized form. The primary outcomes are the following: (1) use of systematic review summaries decision-making (e.g., self-reported use of the evidence in policy-making, decision-making) and (2) policy-maker understanding, knowledge, and/or beliefs (e.g., changes in knowledge scores about the topic included in the summary). We will conduct a systematic review of randomized controlled trials (RCTs), non-randomized controlled trials (NRCTs), controlled before-after studies (CBA), and interrupted time series (ITS) studies. DISCUSSION: The results of this review will inform the development of future systematic review summaries to ensure that systematic review evidence is accessible to and used by policy-makers making health-related decisions.
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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 | 0.241 | 0.397 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.071 | 0.020 |
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".