Ethnic bias amongst medical students in Aotearoa/New Zealand: Findings from the Bias and Decision Making in Medicine (BDMM) study.
Donna CormackRicci HarrisJames StanleyCameron LaceyRhys JonesElana CurtisPublished in: PloS one (2018)
Although health provider racial/ethnic bias has the potential to influence health outcomes and inequities, research within health education and training contexts remains limited. This paper reports findings from an anonymous web-based study examining racial/ethnic bias amongst final year medical students in Aotearoa/New Zealand. Data from 302 students (34% of all eligible final year medical students) were collected in two waves in 2014 and 2015 as part of the Bias and Decision Making in Medicine (BDMM) study. Two chronic disease vignettes, two implicit bias measures, and measures of explicit bias were used to assess racial/ethnic bias towards New Zealand European and Māori (indigenous) peoples. Medical students demonstrated implicit pro-New Zealand European racial/ethnic bias on average, and bias towards viewing New Zealand European patients as more compliant relative to Māori. Explicit pro-New Zealand European racial/ethnic bias was less evident, but apparent for measures of ethnic preference, relative warmth, and beliefs about the compliance and competence of Māori patients relative to New Zealand European patients. In addition, racial/ethnic bias appeared to be associated with some measures of medical student beliefs about individual patients by ethnicity when responding to a mental health vignette. Patterning of racial/ethnic bias by student characteristics was not consistent, with the exception of some associations between student ethnicity, socioeconomic background, and racial/ethnic bias. This is the first study of its kind with a health professional population in Aotearoa/New Zealand, representing an important contribution to further understanding and addressing current health inequities between Māori and New Zealand European populations.
Keyphrases
- medical students
- mental health
- end stage renal disease
- healthcare
- ejection fraction
- chronic kidney disease
- newly diagnosed
- public health
- prognostic factors
- peritoneal dialysis
- primary care
- emergency department
- magnetic resonance
- magnetic resonance imaging
- risk assessment
- machine learning
- quality improvement
- data analysis