SUPPORT Tools for evidence-informed Policymaking in health 11: Finding and using evidence about local conditions
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. Evidence about local conditions is evidence that is available from the specific setting(s) in which a decision or action on a policy or programme option will be taken. Such evidence is always needed, together with other forms of evidence, in order to inform decisions about options. Global evidence is the best starting point for judgements about effects, factors that modify those effects, and insights into ways to approach and address problems. But local evidence is needed for most other judgements about what decisions and actions should be taken. In this article, we suggest five questions that can help to identify and appraise the local evidence that is needed to inform a decision about policy or programme options. These are: 1. What local evidence is needed to inform a decision about options? 2. How can the necessary local evidence be found? 3. How should the quality of the available local evidence be assessed? 4. Are there important variations in the availability, quality or results of local evidence? 5. How should local evidence be incorporated with other information?
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 | 0.040 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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