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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,<sup>1</sup> George Koch,<sup>2</sup> Kyle Rose,<sup>3</sup> Roei Golan,<sup>4</sup> Jennifer Regala,<sup>5</sup> Casey Seideman,<sup>6</sup> Amanda North,<sup>7</sup> Kevin Koo,<sup>8</sup> Kevan Sternberg,<sup>9</sup> Gina Badalato,<sup>10</sup> Benjamin Dropkin,<sup>11</sup> Nicholas Chakiryan,<sup>12</sup> Robert Siemens,<sup>13</sup> Peter Clark,<sup>14</sup> Andrew Harris<sup>11</sup> <h4>Objective</h4> 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 <i>Journal of Urology</i> reviewers. <h4>Design</h4> <i>The Journal of Urology</i> 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. <h4>Results</h4> 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. <h4>Conclusions</h4> 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. <sup>1</sup>Connecticut Children’s, Hartford, CT, US, annedudleymd@gmail.com; <sup>2</sup>The Ohio State University Wexner Medical Center, Columbus, OH, US; <sup>3</sup>Ochsner Medical Center, New Orleans, LA, US; <sup>4</sup>Florida State University School of Medicine, Gainesville, FL, US; <sup>5</sup>Wolters Kluwer Health, Baltimore, MD, US; <sup>6</sup>Doernbecher Children’s Hospital at OHSU, Portland, OR, US; <sup>7</sup>The Children’s Hospital at Montefiore, Bronx, NY, US; <sup>8</sup>Mayo Clinic College of Medicine and Science, Rochester, MN, US; <sup>9</sup>Northwestern Medical Center, Chicago, IL, US; <sup>10</sup>Columbia University, New York, NY, US; <sup>11</sup>University of Kentucky, Lexington, KY, US; <sup>12</sup>H Lee Moffitt Cancer Center, Tampa, FL, US; <sup>13</sup>Queen’s University, Kingston, ON, Canada; <sup>14</sup>Levine Cancer Institute, Charlotte, NC, US. <h4>Conflict of Interest Disclosures</h4> None reported. <h4>Acknowledgment</h4> 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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.013
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0070.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.002

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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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 routes1
Has abstractyes

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