SUPPORT Tools for evidence-informed health Policymaking (STP) 13: Preparing and using policy briefs to support evidence-informed policymaking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it