Strategies for enacting health policy codesign: a scoping review and direction for research
Bibliographic record
Abstract
BACKGROUND: Strategies for supporting evidence-informed health policy are a recognized but understudied area of policy dissemination and implementation science. Codesign describes a set of strategies potentially well suited to address the complexity presented by policy formation and implementation. We examine the health policy literature describing the use of codesign in initiatives intended to combine diverse sources of knowledge and evidence in policymaking. METHODS: The search included PubMed, MEDLINE, PsychInfo, CINAHL, Web of Science, and Google Scholar in November 2022 and included papers published between 1996 and 2022. Terms included codesign, health, policy, and system terminology. Title and abstracts were reviewed in duplicate and included if efforts informed policy or system-level decision-making. Extracted data followed scoping review guidelines for location, evaluation method, health focus, codesign definition, description, level of health system user input, sectors involved, and reported benefits and challenges. RESULTS: From 550 titles, 23 citations describing 32 policy codesign studies were included from multiple continents (Australia/New Zealand, 32%; UK/Europe, 32%; South America, 14%; Africa, 9%; USA/Canada 23%). Document type was primarily case study (77%). The area of health focus was widely distributed. Policy type was more commonly little p policy (47%), followed by big p policy (25%), and service innovations that included policy-enabled funding (25%). Models and frameworks originated from formal design (e.g., human-centered or participatory design (44%), political science (38%), or health service research (16%). Reported outcomes included community mobilization (50%), policy feasibility (41%), improved multisector alignment (31%), and introduction of novel ideas and critical thinking (47%). Studies engaging policy users in full decision-making roles self-reported higher levels of community mobilization and community needs than other types of engagement. DISCUSSION: Policy codesign is theoretically promising and is gaining interest among diverse health sectors for addressing the complexity of policy formation and implementation. The maturity of the science is just emerging. We observed trends in the association of codesign strategies and outcomes that suggests a research agenda in this area could provide practical insights for tailoring policy codesign to respond to local contextual factors including values, needs, and resources.
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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 | 0.270 | 0.397 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.054 | 0.056 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".