Knowledge user involvement is still uncommon in published rapid reviews—a meta-research cross-sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Involving knowledge users (KUs) such as patients, clinicians, or health policymakers is particularly relevant when conducting rapid reviews (RRs), as they should be tailored to decision-makers' needs. However, little is known about how common KU involvement currently is in RRs. OBJECTIVES: We wanted to assess the proportion of KU involvement reported in recently published RRs (2021 onwards), which groups of KUs were involved in each phase of the RR process, to what extent, and which factors were associated with KU involvement in RRs. METHODS: We conducted a meta-research cross-sectional study. A systematic literature search in Ovid MEDLINE and Epistemonikos in January 2024 identified 2,493 unique records. We dually screened the identified records (partly with assistance from an artificial intelligence (AI)-based application) until we reached the a priori calculated sample size of 104 RRs. We dually extracted data and analyzed it descriptively. RESULTS: The proportion of RRs that reported KU involvement was 19% (95% confidence interval [CI]: 12%-28%). Most often, KUs were involved during the initial preparation of the RR, the systematic searches, and the interpretation and dissemination of results. Researchers/content experts and public/patient partners were the KU groups most often involved. KU involvement was more common in RRs focusing on patient involvement/shared decision-making, having a published protocol, and being commissioned. CONCLUSIONS: Reporting KU involvement in published RRs is uncommon and often vague. Future research should explore barriers and facilitators for KU involvement and its reporting in RRs. Guidance regarding reporting on KU involvement in RRs is needed.
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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 | 0.246 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 it