SUPPORT Tools for evidence-informed health Policymaking (STP)
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 the Introduction to a series written for people responsible for making decisions about health policies and programmes and for those who support these decision makers. Knowing how to find and use research evidence can help policymakers and those who support them to do their jobs better and more efficiently. Each article in this series presents a proposed tool that can be used by those involved in finding and using research evidence to support evidence-informed health policymaking. The series addresses four broad areas: 1. Supporting evidence-informed policymaking 2. Identifying needs for research evidence in relation to three steps in policymaking processes, namely problem clarification, options framing, and implementation planning 3. Finding and assessing both systematic reviews and other types of evidence to inform these steps, and 4. Going from research evidence to decisions. Each article begins with between one and three typical scenarios relating to the topic. These scenarios are designed to help readers decide on the level of detail relevant to them when applying the tools described. Most articles in this series are structured using a set of questions that guide readers through the proposed tools and show how to undertake activities to support evidence-informed policymaking efficiently and effectively. These activities include, for example, using research evidence to clarify problems, assessing the applicability of the findings of a systematic review about the effects of options selected to address problems, organising and using policy dialogues to support evidence-informed policymaking, and planning policy monitoring and evaluation. In several articles, the set of questions presented offers more general guidance on how to support evidence-informed policymaking. Additional information resources are listed and described in every article. The evaluation of ways to support evidence-informed health policymaking is a developing field and feedback about how to improve the series is welcome.
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 | 0.050 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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