SUPPORT Tools for evidence-informed health Policymaking (STP) 14: Organising and using policy dialogues 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 dialogues allow research evidence to be considered together with the views, experiences and tacit knowledge of those who will be involved in, or affected by, future decisions about a high-priority issue. Increasing interest in the use of policy dialogues has been fuelled by a number of factors: 1. The recognition of the need for locally contextualised 'decision support' for policymakers and other stakeholders 2. The recognition that research evidence is only one input into the decision-making processes of policymakers and other stakeholders 3. The recognition that many stakeholders can add significant value to these processes, and 4. The recognition that many stakeholders can take action to address high-priority issues, and not just policymakers. In this article, we suggest questions to guide those organising and using policy dialogues to support evidence-informed policymaking. These are: 1. Does the dialogue address a high-priority issue? 2. Does the dialogue provide opportunities to discuss the problem, options to address the problem, and key implementation considerations? 3. Is the dialogue informed by a pre-circulated policy brief and by a discussion about the full range of factors that can influence the policymaking process? 4. Does the dialogue ensure fair representation among those who will be involved in, or affected by, future decisions related to the issue? 5. Does the dialogue engage a facilitator, follow a rule about not attributing comments to individuals, and not aim for consensus? 6. Are outputs produced and follow-up activities undertaken to support action?
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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 | 0.144 | 0.273 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.008 | 0.035 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.038 | 0.024 |
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".