Back

Implicit provider bias in cancer clinical trial enrollment: A scoping review

Beauchemin, M.; Verp, J.; Laforet, P.; Solomon, S.; Walker, D.; Hershman, D. L.; Bentlyewski, E.; Liang, N.-C.; Hillyer, G. C.

2025-06-26 public and global health
10.1101/2025.06.25.25330296 medRxiv
Show abstract

BackgroundThe lack of racial and ethnic representation in cancer clinical trials further exacerbates existing health disparities and has been linked to inequitable and unequal distribution of resources, social determinants of health, racism, and most recently, provider implicit bias. The purpose of this scoping review is to describe the evidence of implicit bias as it relates to cancer clinical trial enrollment. MethodsSystematic methods were followed to search online databases for publications that assessed provider bias and cancer clinical trial offers through August 1, 2023. Variables of interest included how implicit bias was defined or measured, what type of bias was examined, and if provider implicit bias was identified or described in the cancer clinical trial enrollment process. ResultsWe identified 10 publications that met the inclusion criteria. Nine of ten studies were conducted in the United States, and all studies utilized observational or qualitative study design. Most assessed the provider perspective, and one study used a validated measure to identify implicit bias. Some studies identified evidence of implicit provider bias in clinical trial discussions and offers; however, it was observed in situations where system constraints, such as lack of resources or time were present that may be common in oncological settings. ConclusionsMultilevel support and dedication to improving racial, ethnic, and other patient-level representativeness are required to mitigate the influence of implicit bias in resource-poor and stressful clinical settings to ensure equitable access to and enrollment in clinical trials for all patients with cancer. RegistrationThe protocol for this scoping review was registered to Open Science Framework (1), https://osf.io/a6kbt/?view_only=0a857280364a49b9b3f8363b91b00994.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.