SUPPORT Tools for evidence-informed health Policymaking (STP) 2: Improving how your organisation supports the use of research evidence to inform 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. In this article, we address ways of organising efforts to support evidence-informed health policymaking. Efforts to link research to action may include a range of activities related to the production of research that is both highly relevant to--and appropriately synthesised for--policymakers. Such activities may include a mix of efforts used to link research to action, as well as the evaluation of such efforts. Little is known about how best to organise the range of activity options available and, until recently, there have been relatively few organisations responsible for supporting the use of research evidence in developing health policy. We suggest five questions that can help guide considerations of how to improve organisational arrangements to support the use of research evidence to inform health policy decision making. These are: 1. What is the capacity of your organisation to use research evidence to inform decision making? 2. What strategies should be used to ensure collaboration between policymakers, researchers and stakeholders? 3. What strategies should be used to ensure independence as well as the effective management of conflicts of interest? 4. What strategies should be used to ensure the use of systematic and transparent methods for accessing, appraising and using research evidence? 5. What strategies should be used to ensure adequate capacity to employ these methods?
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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 | 0.358 | 0.611 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.055 | 0.057 |
| Open science | 0.010 | 0.036 |
| Research integrity | 0.024 | 0.019 |
| Insufficient payload (model declined to judge) | 0.032 | 0.037 |
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".