Abstract

BackgroundHealth provider racial/ethnic bias and its relationship to clinical decision-making is an emerging area of research focus in understanding and addressing ethnic health inequities. Examining potential racial/ethnic bias among medical students may provide important information to inform medical education and training. This paper describes the development, pretesting and piloting of study content, tools and processes for an online study of racial/ethnic bias (comparing Māori and New Zealand European) and clinical decision-making among final year medical students in New Zealand (NZ).MethodsThe study was developed, pretested and piloted using a staged process (eight stages within five phases). Phase 1 included three stages: 1) scoping and conceptual framework development; 2) literature review and identification of potential measures and items; and, 3) development and adaptation of study content. Three main components were identified to assess different aspects of racial/ethnic bias: (1) implicit racial/ethnic bias using NZ-specific Implicit Association Tests (IATs); (2) explicit racial/ethnic bias using direct questions; and, (3) clinical decision-making, using chronic disease vignettes. Phase 2 (stage 4) comprised expert review and refinement. Formal pretesting (Phase 3) included construct testing using sorting and rating tasks (stage 5) and cognitive interviewing (stage 6). Phase 4 (stage 7) involved content revision and building of the web-based study, followed by pilot testing in Phase 5 (stage 8).ResultsMaterials identified for potential inclusion performed well in construct testing among six participants. This assisted in the prioritisation and selection of measures that worked best in the New Zealand context and aligned with constructs of interest. Findings from the cognitive interviewing (nine participants) on the clarity, meaning, and acceptability of measures led to changes in the final wording of items and ordering of questions. Piloting (18 participants) confirmed the overall functionality of the web-based questionnaire, with a few minor revisions made to the final study.ConclusionsRobust processes are required in the development of study content to assess racial/ethnic bias in order to optimise the validity of specific measures, ensure acceptability and minimise potential problems. This paper has utility for other researchers in this area by informing potential development approaches and identifying possible measurement tools.Electronic supplementary materialThe online version of this article (doi:10.1186/s12909-016-0701-6) contains supplementary material, which is available to authorized users.

Highlights

  • Health provider racial/ethnic bias and its relationship to clinical decision-making is an emerging area of research focus in understanding and addressing ethnic health inequities

  • It focused on dimensions of racial/ethnic bias identified in the work of van Ryn et al [9], p201, namely beliefs, attitudes, feelings and behaviours towards individuals or groups in respect of their race/ethnicity

  • The 18 words tested for use in the ethnicity and compliant patient Implicit Association Test (IAT) as representing the compliant and reluctant

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Summary

Introduction

Health provider racial/ethnic bias and its relationship to clinical decision-making is an emerging area of research focus in understanding and addressing ethnic health inequities. Examining potential racial/ethnic bias among medical students may provide important information to inform medical education and training. Racism can be conceptualised as a societal phenomenon involving ideologies about ‘racial’ and ‘ethnic’ groups grounded in particular histories and socio-political contexts [5]. Within this social system, the categories of ‘race’ and ‘ethnicity’ are constructed, racial hierachies are created and maintained, and manifest as discrimination at personal and structural levels [6, 7]. There are multiple ways by which racism impacts on health both directly and indirectly [4], including the potential impact of racial/ ethnic bias amongst healthcare providers [8]

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