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Record W7115690132 · doi:10.48448/5f1b-ad41

[V] Experiences and Challenges Faced by Canadian Health Research Grant Peer Reviewers

2025· other· W7115690132 on OpenAlexaffabout

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeer reviewResearch ethicsInclusion (mineral)Qualitative researchPublic healthPopulationInformed consentCompetition (biology)Health care

Abstract

fetched live from OpenAlex

Joanie Sims Gould,1 Anne Lasinsky,2 Adrian Mota,3 Karim M. Khan,1,2,4 Clare L. Ardern5,6 Objective 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. Design 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.1 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. Results 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. Conclusions 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. Reference 1. Srivastava A, Thomson SB. Framework analysis: a qualitative methodology for applied policy research. Journal of Administration and Governance. 2009;72. Accessed May 31, 2025. https://ssrn.com/abstract=2760705 1Department of Family Practice, The University of British Columbia, Vancouver, British Columbia, Canada; 2School of Kinesiology, The University of British Columbia, Vancouver, British Columbia, Canada; 3Canadian Institutes of Health, Ottawa, Ontario, Canada; 4Canadian Institutes of Health–Institute of Musculoskeletal Health and Arthritis, Vancouver, British Columbia, Canada; 5Department of Physical Therapy, The University of British Columbia, Vancouver, British Columbia, Canada, clare.ardern@ubc.ca; 6Sport and Exercise Medicine Research Centre, La Trobe University, Melbourne, Victoria, Australia. Conflict of Interest Disclosures 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. Funding/Support This work was supported by a CIHR research operating grant (scientific directors) held by Karim M. Khan. Role of the Funder/Sponsor 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.184
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.591

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.184
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0510.018
Scholarly communication0.0200.008
Open science0.0060.014
Research integrity0.0070.008
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.210
GPT teacher head0.422
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainEvaluation
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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