Assessing how information is packaged in rapid reviews for policy-makers and other stakeholders: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Rapid reviews (RRs) are useful products to healthcare policy-makers and other stakeholders, who require timely evidence. Therefore, it is important to assess how well RRs convey useful information in a format that is easy to understand so that decision-makers can make best use of evidence to inform policy and practice. METHODS: We assessed a diverse sample of 103 RRs against the BRIDGE criteria, originally developed for communicating clearly to support healthcare policy-making. We modified the criteria to increase assessability and to align with RRs. We identified RRs from key database searches and through searching organisations known to produce RRs. We assessed each RR on 26 factors (e.g. organisation of information, lay language use). Results were descriptively analysed. Further, we explored differences between RRs published in journals and those published elsewhere. RESULTS: Certain criteria were well covered across the RRs (e.g. all aimed to synthesise research evidence and all provided references of included studies). Further, most RRs provided detail on the problem or issue (96%; n = 99) and described methods to conduct the RR (91%; n = 94), while several addressed political or health systems contexts (61%; n = 63). Many RRs targeted policy-makers and key stakeholders as the intended audience (66%; n = 68), yet only 32% (n = 33) involved their tacit knowledge, while fewer (27%; n = 28) directly involved them reviewing the content of the RR. Only six RRs involved patient partners in the process. Only 23% (n = 24) of RRs were prepared in a format considered to make information easy to absorb (i.e. graded entry) and 25% (n = 26) provided specific key messages. Readability assessment indicated that the text of key RR sections would be hard to understand for an average reader (i.e. would require post-secondary education) and would take 42 (± 36) minutes to read. CONCLUSIONS: Overall, conformity of the RRs with the modified BRIDGE criteria was modest. By assessing RRs against these criteria, we now understand possible ways in which they could be improved to better meet the information needs of healthcare decision-makers and their potential for innovation as an information-packaging mechanism. The utility and validity of these items should be further explored. PROTOCOL AVAILABILITY: The protocol, published on the Open Science Framework, is available at: osf.io/68tj7.
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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 | 0.128 | 0.353 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".