Primary Author Characteristics Associated With Publication in the Journal of Pain and Symptom Management

J Pain Symptom Manage. 2024 Feb;67(2):105-111.e1. doi: 10.1016/j.jpainsymman.2023.10.014. Epub 2023 Oct 18.

Abstract

Context: Scientific journals are the primary source for dissemination of research findings, and this process relies on rigorous editorial and peer-review. As part of continuing efforts by the Journal of Pain and Symptom Management (JPSM) to advance equity, diversity, and inclusion, JPSM's leadership requested an external evaluation of their publication decisions.

Objectives: 1) Describe primary author characteristics associated with final decisions to accept or reject manuscripts submitted for publication; 2) Report on whether there are potential publication biases in the JPSM editorial or peer-review processes.

Methods: Data consisted of self-reported primary author demographic characteristics associated with manuscript submissions between June 18, 2020, and December 31, 2022. Characteristics included region of residence, race, gender, and ethnicity. A multiple logistic regression model was used to estimate adjusted odds of rejection for each author characteristic.

Results: A total of 1940 submissions were evaluated. Compared to authors residing in North America, authors residing in Asia had six-fold greater odds of rejection, authors residing in Europe had four-fold greater odds of rejection, and authors residing in other regions had two-fold greater odds of rejection. Female authors submitted 1.7 times more papers than males, but there was no difference in acceptance rates of their papers in adjusted analysis.

Conclusion: In this analysis of publication decisions by the JPSM, there were differences in acceptance rates by region of residence, ethnicity, and race but not by gender. Asian authors and authors residing in regions outside of North America had greater odds of rejection compared to White or North American authors.

Keywords: Diversity; equity; inclusion; publication bias.

MeSH terms

  • Europe
  • Female
  • Humans
  • Logistic Models
  • Male
  • Multivariate Analysis
  • Pain*