Peer review of health research funding proposals: A systematic map and systematic review of innovations for effectiveness and efficiency
Bibliographic record
Abstract
OBJECTIVE: To investigate methods and processes for timely, efficient and good quality peer review of research funding proposals in health. METHODS: A two-stage evidence synthesis: (1) a systematic map to describe the key characteristics of the evidence base, followed by (2) a systematic review of the studies stakeholders prioritised as relevant from the map on the effectiveness and efficiency of peer review 'innovations'. Standard processes included literature searching, duplicate inclusion criteria screening, study keyword coding, data extraction, critical appraisal and study synthesis. RESULTS: A total of 83 studies from 15 countries were included in the systematic map. The evidence base is diverse, investigating many aspects of the systems for, and processes of, peer review. The systematic review included eight studies from Australia, Canada, and the USA, evaluating a broad range of peer review innovations. These studies showed that simplifying the process by shortening proposal forms, using smaller reviewer panels, or expediting processes can speed up the review process and reduce costs, but this might come at the expense of peer review quality, a key aspect that has not been assessed. Virtual peer review using videoconferencing or teleconferencing appears promising for reducing costs by avoiding the need for reviewers to travel, but again any consequences for quality have not been adequately assessed. CONCLUSIONS: There is increasing international research activity into the peer review of health research funding. The studies reviewed had methodological limitations and variable generalisability to research funders. Given these limitations it is not currently possible to recommend immediate implementation of these innovations. However, many appear promising based on existing evidence, and could be adapted as necessary by funders and evaluated. Where feasible, experimental evaluation, including randomised controlled trials, should be conducted, evaluating impact on effectiveness, efficiency and quality.
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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 | 0.560 | 0.753 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.019 | 0.014 |
| Bibliometrics | 0.075 | 0.057 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.033 | 0.052 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".