[V] Experiences and Challenges Faced by Canadian Health Research Grant Peer Reviewers
Bibliographic record
Abstract
Joanie Sims Gould,<sup>1</sup> Anne Lasinsky,<sup>2</sup> Adrian Mota,<sup>3</sup> Karim M. Khan,<sup>1,2,4</sup> Clare L. Ardern<sup>5,6</sup> <h4>Objective</h4> There is robust debate about the perceived strengths and weakness of grant peer review. Much of the research on issues in grant peer review is based on quantitative analysis of funding or scoring outcomes, which does not illuminate the experiences of peer review committee members. The objective of this study was to explore and understand the experiences and challenges faced by Canadian health research grant peer reviewers. <h4>Design</h4> This qualitative study received ethics approval from the University Behavioural Research Ethics Board. Study conduct and reporting followed the Consolidated Criteria for Reporting Qualitative Research (COREQ) guideline. Chairs, peer reviewers, and Scientific Officers of the Canadian Institutes of Health Research (CIHR) project grant competition peer review panels were interviewed. CIHR staff prepared a list of 50 potential participants who represented the 4 primary branches of CIHR research (biomedical, clinical, health systems and services, population health) from a public website. The inclusion criterion was having participated in a CIHR Project Grant competition peer review panel at least once as a peer reviewer, chair, or scientific officer. The researchers randomly selected names from the list and sent a recruitment email inviting participants to an online semistructured interview. The response rate was 36%. Two experienced qualitative researchers recruited and interviewed participants on a rolling basis from February to August 2022. The study team met biweekly to review the interview transcripts. All participants provided verbal consent for audio recording and reporting of quotes at the beginning of the interview. The interview guide was developed based on a priori concepts of peer review and the study team’s research experience in the field of grant peer review. There were questions about participants’ background, training in peer review, strengths and challenges of the review process, and managing conflict and bias. The analysis used a framework analysis approach.<sup>1</sup> Data were sifted, charted, and sorted based on key issues and themes to identify a thematic framework, compare and contrast themes, and explore similarities and differences. <h4>Results</h4> Eighteen participants were interviewed, all of whom were mid- or senior-career researchers (age 42-77 years); 11 participants (61.1%) were women and 7 (38.9%) were men. Twelve participants (66.7%) identified as White, 3 (16.7%) as South Asian, and 3 (16.7%) as other race or ethnicity. Participants identified 3 threats to grant peer review: (1) lack of training and limited opportunities to learn, (2) challenges in differentiating and rating applications of similar strength, and (3) relying on academic reputations and personal relationships in the review process to differentiate grant applications of a similar rating. <h4>Conclusions</h4> Experienced grant peer reviewers in the Canadian health funding context identified an absence of training and learning opportunities for peer review, difficulty differentiating between applications of similar strength, and an emphasis on academic reputations and personal relationships in rating applications for the Project Grant Competition. <h4>Reference</h4> 1. Srivastava A, Thomson SB. Framework analysis: a qualitative methodology for applied policy research. <i>Journal of Administration and Governance</i>. 2009;72. Accessed May 31, 2025. <a href="https://ssrn.com/abstract=2760705"><span class="Hyperlink CharOverride-6">https://ssrn.com/abstract=2760705</span></a> <sup>1</sup>Department of Family Practice, The University of British Columbia, Vancouver, British Columbia, Canada; <sup>2</sup>School of Kinesiology, The University of British Columbia, Vancouver, British Columbia, Canada; <sup>3</sup>Canadian Institutes of Health, Ottawa, Ontario, Canada; <sup>4</sup>Canadian Institutes of Health–Institute of Musculoskeletal Health and Arthritis, Vancouver, British Columbia, Canada; <sup>5</sup>Department of Physical Therapy, The University of British Columbia, Vancouver, British Columbia, Canada, clare.ardern@ubc.ca; <sup>6</sup>Sport and Exercise Medicine Research Centre, La Trobe University, Melbourne, Victoria, Australia. <h4>Conflict of Interest Disclosures</h4> Adrian Mota is acting vice president, Research–Programs for the Canadian Institute of Health Research (CIHR). Karim M. Khan is scientific director for the CIHR Institute of Musculoskeletal Health and Arthritis (2017-2025). No other disclosures were reported. <h4>Funding/Support </h4> This work was supported by a CIHR research operating grant (scientific directors) held by Karim M. Khan. <h4>Role of the Funder/Sponsor</h4> The CIHR had no role in design and conduct of the study. The CIHR did not participate in interpretation of the data or the preparation of the abstract and did not participate in the decision to submit the abstract for presentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads 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".