Rapid review programs to support health care and policy decision making: a descriptive analysis of processes and methods
Bibliographic record
Abstract
BACKGROUND: Health care decision makers often need to make decisions in limited timeframes and cannot await the completion of a full evidence review. Rapid reviews (RRs), utilizing streamlined systematic review methods, are increasingly being used to synthesize the evidence with a shorter turnaround time. Our primary objective was to describe the processes and methods used internationally to produce RRs. In addition, we sought to understand the underlying themes associated with these programs. METHODS: We contacted representatives of international RR programs from a broad realm in health care to gather information about the methods and processes used to produce RRs. The responses were summarized narratively to understand the characteristics associated with their processes and methods. The summaries were compared and contrasted to highlight potential themes and trends related to the different RR programs. RESULTS: Twenty-nine international RR programs were included in our sample with a broad organizational representation from academia, government, research institutions, and non-for-profit organizations. Responses revealed that the main objectives for RRs were to inform decision making with regards to funding health care technologies, services and policy, and program development. Central themes that influenced the methods used by RR programs, and report type and dissemination were the imposed turnaround time to complete a report, resources available, the complexity and sensitivity of the research topics, and permission from the requestor. CONCLUSIONS: Our study confirmed that there is no standard approach to conduct RRs. Differences in processes and methods across programs may be the result of the novelty of RR methods versus other types of evidence syntheses, customization of RRs for various decision makers, and definition of 'rapid' by organizations, since it impacts both the timelines and the evidence synthesis methods. Future research should investigate the impact of current RR methods and reporting to support informed health care decision making, the effects of potential biases that may be introduced with streamlined methods, and the effectiveness of RR reporting guidelines on transparency.
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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 | 0.281 | 0.492 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.031 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".