What happened next? A survey of review clients evaluating impacts of rapid reviews
Bibliographic record
Abstract
OBJECTIVES: End-user evaluation of the impact of evidence syntheses is critical to demonstrating value. This study presents results of a survey evaluating the impact of rapid reviews undertaken by two teams based in Melbourne, Australia, and Hamilton, Canada. METHODS: Clients were invited to participate in a short written survey following delivery of a rapid review. Survey items encompassed reach, usefulness and format; interactions with the review teams; and overall satisfaction. RESULTS: Twenty-five completed surveys from 53 invitations were received pertaining to 19 rapid reviews conducted between September 2021 and October 2023. Topics encompassed COVID-19, health and behavior change; reports were an average of 31 pages; and were delivered over an average of 62 days. Evaluation findings were positive, with high satisfaction with reports and service delivery; very high satisfaction with report structure and length; good evidence of reach (reports read by decision makers and cited in other documents); and evidence that the rapid reviews made contributions to strategic planning, policy and program funding decisions. CONCLUSION: Rapid reviews are making impactful contributions, alongside other inputs, to policy and practice. Further research is required to build this evaluation dataset; examine the balance between timeliness and methodological rigor in evidence synthesis; and explore models of delivery and capacity within and outside of government. It is also critical to promote implementation efforts to harness the full potential of rapid reviews and other evidence syntheses to impact the lives of citizens.
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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 | 0.136 | 0.482 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".