Designing a rapid response program to support evidence-informed decision-making in the Americas region: using the best available evidence and case studies
Bibliographic record
Abstract
BACKGROUND: The objective of this work was to inform the design of a rapid response program to support evidence-informed decision-making in health policy and practice for the Americas region. Specifically, we focus on the following: (1) What are the best methodological approaches for rapid reviews of the research evidence? (2) What other strategies are needed to facilitate evidence-informed decision-making in health policy and practice? and (3) How best to operationalize a rapid response program? METHODS: The evidence used to inform the design of a rapid response program included (i) two rapid reviews of methodological approaches for rapid reviews of the research evidence and strategies to facilitate evidence-informed decision-making, (ii) supplementary literature in relation to the "shortcuts" that could be considered to reduce the time needed to complete rapid reviews, (iii) four case studies, and (iv) supplementary literature to identify additional operational issues for the design of the program. RESULTS: There is no agreed definition of rapid reviews in the literature and no agreed methodology for conducting them. Better reporting of rapid review methods is needed. The literature found in relation to shortcuts will be helpful in choosing shortcuts that maximize timeliness while minimizing the impact on quality. Evidence for other strategies that can be used concurrently to facilitate the uptake of research evidence, including evidence drawn from rapid reviews, is presented. Operational issues that need to be considered in designing a rapid response program include the implications of a "user-pays" model, the importance of recruiting staff with the right mix of skills and qualifications, and ensuring that the impact of the model on research use in decision-making is formally evaluated. CONCLUSIONS: When designing a new rapid response program, greater attention needs to be given to specifying the rapid review methods and reporting these in sufficient detail to allow a quality assessment. It will also be important to engage in other strategies to facilitate the uptake of the rapid reviews and to evaluate the chosen model in order to make refinements and add to the evidence base for evidence-informed decision-making.
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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 | 0.518 | 0.596 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".