Co-design workshops to develop evidence synthesis summary formats for use by clinical guideline development groups
Bibliographic record
Abstract
BACKGROUND: Evidence synthesis is used by decision-makers in various ways, such as developing evidence-based recommendations for clinical guidelines. Clinical guideline development groups (GDGs) typically discuss evidence synthesis findings in a multidisciplinary group, including patients, healthcare providers, policymakers, etc. A recent mixed methods systematic review (MMSR) identified no gold standard format for optimally presenting evidence synthesis findings to these groups. However, it provided 94 recommendations to help produce more effective summary formats for general evidence syntheses (e.g., systematic reviews). To refine the MMSR recommendations to create more actionable guidance for summary producers, we aimed to explore these 94 recommendations with participants involved in evidence synthesis and guideline development. METHODS: We conducted a descriptive qualitative study using online focus group workshops in February and March 2023. These groups used a participatory co-design approach with interactive voting activities to identify preferences for a summary format's essential content and style. We created a topic guide focused on recommendations from the MMSR with mixed methods support, ≥ 3 supporting studies, and those prioritized by an expert advisory group via a pragmatic prioritization exercise using the MoSCoW method (Must, Should, Could, and Will not haves). Eligible participants must be/have been involved in GDGs and/or evidence synthesis. Groups were recorded and transcribed. Two independent researchers analyzed transcripts using directed content analysis with 94 pre-defined codes from the MMSR. RESULTS: Thirty individuals participated in six focus groups. We coded 79 of the 94 pre-defined codes. Participants suggested a "less is more" structured approach that minimizes methodological steps and statistical data, promoting accessibility to all audiences by judicious use of links to further information in the full report. They emphasized concise, consistently presented formats that highlight key messages, flag readers to indicators of trust in the producers (i.e., logos, websites, and conflict of interest statements), and highlight the certainty of evidence (without extenuating details). CONCLUSIONS: This study identified guidance based on the preferences of guideline developers and evidence synthesis producers about the format of evidence synthesis summaries to support decision-making. The next steps involve developing and user-testing prototype formats through one-on-one semi-structured interviews to optimize evidence synthesis summaries and support decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.093 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.007 |
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; both teacher heads agree on what is shown here.
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".