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Record W4400412286 · doi:10.1002/jhm.13457

Your service, our gratitude: A thank you to our peer reviewers

2024· article· en· W4400412286 on OpenAlexaff
Samir S. Shah, Gregory W. Ruhnke, Sanjay Mahant, Daniel J. Brotman, Farah Acher Kaiksow, Charlie M. Wray

Bibliographic record

VenueJournal of Hospital Medicine · 2024
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsGratitudeMedicineService (business)Medical educationWorld Wide WebPsychologyComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The peer review process remains a cornerstone of maintaining the integrity of published research and informs editorial decisions regarding manuscript disposition. Each manuscript receives rigorous evaluation by expert reviewers, who contribute insights on its novelty, originality, scientific validity, and potential impact. These reviewers also offer valuable suggestions for manuscript improvement. This structured review process provides authors with constructive feedback, fosters a sense of community, and facilitates the publication of advancements in research and patient care. To augment our in-depth editorial assessment, we aim to have at least two independent peer reviews for each manuscript being considered for publication. However, over the past 3 years, we have had increasing difficulty securing reviewers. The average number of invitations we send to receive two quality reviews has increased from four in 2021 to eight this past year. As clinicians and researchers who regularly perform peer reviews for other journals, we understand the challenge of balancing this service with other obligations. On behalf of the entire journal editorial leadership team, we would like to extend our heartfelt thanks to our peer reviewers. Your reviews significantly enhance our authors' work and we deeply appreciate the time and effort you dedicate to the Journal of Hospital Medicine community. Your support is essential to our ability to publish such exceptional content. We would like to highlight some changes we've made to improve our process and your experience as reviewers for the Journal of Hospital Medicine. We have reduced the demand on our reviewing community. The Editor-in-Chief or other editorial leadership team members assess each manuscript submitted to the journal. This initial screening process has allowed us to only send about 25% of research manuscripts for external peer review. To show our appreciation, our “Journal of Hospital Medicine Top Reviewer” list recognizes the top 10% of reviewers, a designation awarded based on the combination of the number, quality, and turnaround time of reviews. We encourage recipients to list this honor on their curriculum vitae and share the recognition with their program, division, and department leadership. These contributions should also be highlighted in promotion letters as important service to the field and national acknowledgment of expertise. We also offer continuing medical education credit for peer review, underscoring its value in professional development. As always, we welcome your feedback on our peer review process. We are committed to building capacity for peer review in hospital medicine. In 2018, we established the Journal of Hospital Medicine Editorial Fellowship, a year-long mentored experience that helps early career hospitalists develop essential academic skills in peer review, academic writing, and scientific communication.1, 2 To date, this program has trained 36 hospital medicine physicians across the United States. Additionally, the journal proudly supports and encourages experienced reviewers to mentor fellows or junior faculty in reviewing. Prior permission for mentored reviews is not necessary. We ask mentors to let us know who assisted with the peer review. This communication allows us to thank all who participated in the review for their service to the journal and contribution to the field. We believe peer review is integral to sustaining our community and advancing knowledge in our field. Both are essential in allowing clinicians to provide the best care possible to patients and their families. To all our peer reviewers, we sincerely appreciate your efforts. To those interested in becoming a peer reviewer, we welcome your expertise, experience, and insights. Please email us at [email protected] with “I want to become a peer reviewer” in the subject line and we will help you join the Journal of Hospital Medicine community. The authors have nothing to report. The authors declare no conflict of interest.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.886
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.377
Teacher spread0.337 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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
Published2024
Admission routes1
Has abstractyes

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