Identifying approaches for synthesizing and summarizing information to support informed citizen deliberations in health policy: a scoping review
Bibliographic record
Abstract
Objective Public deliberations are an increasingly popular tool to engage citizens in the development of health policies and programmes. However, limited research has been conducted on how to best synthesize and summarize information on health policy issues for citizens. To begin to address this gap, our aim was to map the literature on the preparation of information to support informed citizen deliberations related to health policy issues. Methods We conducted a scoping review where two reviewers screened the results of electronic database searches, grey literature searches and hand searches of organizational websites to identify empirical studies, scholarly commentaries, and publicly available organizational documents focused on synthesizing and summarizing information to inform citizen deliberation about health policy issues. Two reviewers categorized each included document according to themes/topics of deliberation, purpose of deliberation and the form of deliberation, and developed a summary of the key findings related to synthesizing and summarizing information to support informed citizen deliberations. Results There was limited reporting about whether and how information was synthesized. Evidence was typically organized based on the source used (e.g. by comparing the views of stakeholders or experts) or according to the areas that policymakers need to consider when making decisions (e.g. benefits, harms, costs and stakeholder perspectives related to policy options). Information was presented primarily through written materials (e.g. briefs and brochures), audiovisual resources (e.g. videos or presentations from stakeholders), but some interactive presentation approaches were also identified (e.g. through interactive arts-based approaches). Conclusions The choice and framing of information to inform citizen deliberations about health policy can strongly influence their understanding of a policy issue, and has the potential to impact the discussions and recommendations that emerge from deliberations. Our review confirmed that there remains a dearth of literature describing methods of the preparation of information to inform citizen deliberations about health policy issues. This highlights the need for further exploration of optimal strategies for citizen-friendly approaches to synthesizing and summarizing information for deliberations.
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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 | 0.365 | 0.608 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.090 | 0.061 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".