Research and Advice Giving: A Functional View of Evidence‐Informed Policy Advice in a Canadian Ministry of Health
Bibliographic record
Abstract
CONTEXT: As evidence-based medicine grows in influence and scope, its applicability to health policy prompts two questions: Can the principles and, more specifically, the tools used to bring research into the clinical world apply to civil servants offering advice to politicians? If not, what approach should the evidence-oriented health policy organization take to improve the use of research? METHODS: This article reviews evidence-based medicine and models of research use in policy. Then it reports the results of interviews with civil servants in the Ontario Ministry of Health, which recently adopted a stewardship rather than an operational role, incorporating many evidence-oriented strategies. The interviews focused on functional roles for research-based evidence in policy advice. FINDINGS: The clinical context and tools for evidence-based medicine can rarely be generalized to policy. Most current models of research use offer lessons to researchers wishing to apply their work to policy but little help for civil servants wishing to become more evidence oriented. The interviews revealed functional roles for research in setting agendas (noting upcoming issues and screening interest groups' claims), developing new policies (reducing uncertainty, helping speak truth to power, and preventing repetition and duplication), and monitoring or modifying existing policies (continuously improving programs and creating a culture of inquiry). Each area requires different tools to help filter the push of evidence from researchers and set agendas, to facilitate the urgent pull on relevant research by civil servants developing new policy, and to support ongoing linkage and exchange between civil servants and researchers for monitoring and modifying existing policy. CONCLUSIONS: A functional framework for evidence-informed policy advice is useful for distinguishing the activity from evidence-based medicine and "auditing" the balance of efforts across the different functional roles of research in policy.
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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 | 0.095 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.052 | 0.125 |
| Scholarly communication | 0.037 | 0.011 |
| Open science | 0.010 | 0.017 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".