SUPPORT Tools for evidence-informed health Policymaking (STP) 13: Preparing and using policy briefs to support evidence-informed policymaking
Bibliographic record
Abstract
This article is part of a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Policy briefs are a relatively new approach to packaging research evidence for policymakers. The first step in a policy brief is to prioritise a policy issue. Once an issue is prioritised, the focus then turns to mobilising the full range of research evidence relevant to the various features of the issue. Drawing on available systematic reviews makes the process of mobilising evidence feasible in a way that would not otherwise be possible if individual relevant studies had to be identified and synthesised for every feature of the issue under consideration. In this article, we suggest questions that can be used to guide those preparing and using policy briefs to support evidence-informed policymaking. These are: 1. Does the policy brief address a high-priority issue and describe the relevant context of the issue being addressed? 2. Does the policy brief describe the problem, costs and consequences of options to address the problem, and the key implementation considerations? 3. Does the policy brief employ systematic and transparent methods to identify, select, and assess synthesised research evidence? 4. Does the policy brief take quality, local applicability, and equity considerations into account when discussing the synthesised research evidence? 5. Does the policy brief employ a graded-entry format? 6. Was the policy brief reviewed for both scientific quality and system relevance?
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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 | 0.419 | 0.675 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.027 | 0.018 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.042 | 0.040 |
| Open science | 0.011 | 0.035 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.050 | 0.032 |
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".