Three years of quality assurance data assessing the performance of over 4000 grant peer review contributions to the Canadian Institutes of Health Research Project Grant Competition
Bibliographic record
Abstract
The Canadian Institutes of Health Research (CIHR) commenced a Quality Assurance Program in 2019 to monitor the quality of peer review in its Project Grant Competition Peer Review Committees. Our primary aim was to describe the performance of CIHR grant peer reviewers, based on the assessments made by CIHR peer review leaders during the first 3 years of the Research Quality Assurance Program. All Peer Review Committee Chairs and (or) Scientific Officers who led peer review for CIHR in 2019, 2020, and 2021 completed Reviewer Quality Feedback forms immediately following Peer Review Committee meetings. The form assessed Performance, Future potential, Review quality, Participation, and Responsiveness. We summarised and descriptively synthesised data from assessments conducted after each of the four grant competitions. The performance of peer reviewers on 4438 occasions was rated by Chairs and Scientific Officers. Approximately one in three peer reviewers submitted outstanding reviews or discussed additional applications and one in 10 demonstrated potential as a future Peer Review Committee leader. At most, one in 20 peer reviewers was considered to have not performed adequately with respect to review quality, participation, or responsiveness. There is a need for more research on the processes involved in allocating research grant funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.320 | 0.613 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.034 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".