Exploring Bias in Scientific Peer Review: An ASCO Initiative

JCO Oncol Pract. 2022 Dec;18(12):791-799. doi: 10.1200/OP.22.00275. Epub 2022 Oct 10.

Abstract

Purpose: To investigate implicit bias (IB) in the peer review process across ASCO and Conquer Cancer Foundation and to propose potential mitigation strategies.

Materials and methods: We, ASCO Working Group on Implicit Bias, selected four data sources: (1) literature search [(a) defining IB in peer review, (b) evidence of IB in peer review, and (c) strategies to mitigate IB]; (2) created and analyzed an ASCO database for sex, race, and institutional affiliation regarding peer review success; (3) constructed and conducted qualitative interviews of key stakeholders within the ASCO board, publications, and grants committee, on experience with IB within ASCO; and (4) constructed, delivered, and analyzed results of member survey on perception of IB within ASCO.

Results: Historically uncommon, PubMed articles on IB in peer review subsequently increased exponentially in the past 2 decades. Qualitative interviews of ASCO key stakeholders reveal that system changes and IB training were priorities. The committee member survey reported that their peer review decisions could be affected by IB and that mitigating IB should be a priority. Most reported having never been trained on IB. Available data from ASCO database support stakeholder findings, suggesting that there exists a disproportionate representation of males and better-known institutions among both reviewer positions and awardees. Ethnicity/race data were insufficiently reported. Limited data on interventions/strategies to mitigate IB in the peer-reviewed literature suggest that there are feasible processes for grants, program committees, and journals.

Conclusion: Limited data reveal that the peer review process at ASCO is not exempt from IB and suggest association with sex and institutional affiliation. Working Group on Implicit Bias recommends three actions to mitigate IB within peer review: (1) create awareness and a culture of inclusivity, (2) create systems to reduce IB, and (3) collect data for ongoing analysis.

MeSH terms

  • Humans
  • Male
  • Neoplasms*
  • Peer Review*
  • Surveys and Questionnaires