MétaCan
Menu
Back to cohort
Record W7115676258 · doi:10.48448/q90p-mk62

[V] Motivations to Participate in the Peer Review Process at the Journal of Urology

2025· other· W7115676258 on OpenAlexaboutno aff

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPeer reviewLimitingMEDLINEProcess (computing)Work (physics)Session (web analytics)

Abstract

fetched live from OpenAlex

Anne G. Dudley,1 George Koch,2 Kyle Rose,3 Roei Golan,4 Jennifer Regala,5 Casey Seideman,6 Amanda North,7 Kevin Koo,8 Kevan Sternberg,9 Gina Badalato,10 Benjamin Dropkin,11 Nicholas Chakiryan,12 Robert Siemens,13 Peter Clark,14 Andrew Harris11 Objective Peer review is a critical aspect of academic publishing, yet the process takes significant time and energy for the reviewer and is a voluntary activity. Current surveys report high levels of urologist burnout, and recent events, including the COVID-19 pandemic, have led to a shift toward personal priorities outside of work potentially limiting reviewer pools. Within urology, editors report difficulty finding appropriate numbers of peer reviewers for submitted manuscripts. We sought to assess motivations to participate in the peer review process within a pool of recent Journal of Urology reviewers. Design The Journal of Urology partnered with members of the American Urologic Association publications team to develop and administer a web-based survey to a diverse group of reviewers from September 1 to December 31, 2023. All authors and reviewers over the preceding 3 years were invited to participate. The survey addressed various aspects including career stage, their experience as reviewers, and peer review process challenges, incentives, motivators, and feedback needs. Results Respondents (n = 275) completed an average of 9 reviews in the past 12 months and reported 16 years of experience as reviewers. Most reviewers were experienced urologists less than 11 years from training (64% [176]) with only 7% (18) currently in training (resident/fellow). Time emerged as a key variable with 86% (236) of respondents declining additional reviews due to time constraints. A total of 67% (184) of respondents reported reviewing time was worthwhile, yet only 35% (96) felt appropriately recognized for time and effort, and 55% (151) reported incentives would increase time spent on a peer review. Motivations to review included “to give back” (80% [220]), “to learn” (71% [195]), “to get involved” (61% [168]), and “to grow my career” (39% [107]). Most respondents (91% [250]) read other reviewers’ reviews to learn. When asked to select specific incentives to review more papers, American Urologic Association products such as waived meeting fees and membership were highly valued (64% [176]; 62% [170]), followed by recognition by local department leadership (43% [118]) and money (40% [109]). Only 14% (38) of respondents desired gear or swag, and only 23% (63) desired to be named in the journal alongside the manuscript. Conclusions Peer review motivations are diverse and suggest that urologists participate for professional development and an ongoing desire to learn and participate in the field as a whole. Study limitations include nonresponder bias, limited survey period, and lack of granular data on personal and professional motivators. Time remains an important constraint, but incentives may increase allocated time for academic pursuits. Professional meeting/membership fee waivers may be motivators to increase participation. Local efforts to recognize reviewers within departments may work synergistically to increase available reviewers and fulfill career development goals. 1Connecticut Children’s, Hartford, CT, US, annedudleymd@gmail.com; 2The Ohio State University Wexner Medical Center, Columbus, OH, US; 3Ochsner Medical Center, New Orleans, LA, US; 4Florida State University School of Medicine, Gainesville, FL, US; 5Wolters Kluwer Health, Baltimore, MD, US; 6Doernbecher Children’s Hospital at OHSU, Portland, OR, US; 7The Children’s Hospital at Montefiore, Bronx, NY, US; 8Mayo Clinic College of Medicine and Science, Rochester, MN, US; 9Northwestern Medical Center, Chicago, IL, US; 10Columbia University, New York, NY, US; 11University of Kentucky, Lexington, KY, US; 12H Lee Moffitt Cancer Center, Tampa, FL, US; 13Queen’s University, Kingston, ON, Canada; 14Levine Cancer Institute, Charlotte, NC, US. Conflict of Interest Disclosures None reported. Acknowledgment We thank Martha Keyes and the Journal of Urology publications staff for their assistance with this initiative.

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)0.023
metaresearch head score (Gemma)0.218
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.218
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0110.004
Scholarly communication0.0110.005
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0460.017

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 head0.069
GPT teacher head0.399
Teacher spread0.330 · 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 designObservational
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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueUnderline Science Inc.French-language works237,207