ArticleResearch square2026
Investigating Gendered Implicit Bias Among Healthcare Workers in South Africa: Development and Piloting of an Implicit Association Test in a High Tuberculosis and HIV Setting.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: In sub-Saharan Africa, men with HIV and/or tuberculosis (TB) report negative experiences within health services and demonstrate poorer engagement and treatment outcomes compared to women. We developed and piloted an Implicit Association Test (IAT) to assess healthcare workers' (HCWs') gender-based associations characterizing patients with the concepts of "good" versus "bad." Methods: Standardized photographs of Black male and female faces from the Chicago Face Database were paired with locally derived descriptors of "good" and "bad" patients. The IAT was piloted among nurses providing TB or TB/HIV services in government clinics in Eastern Cape Province, South Africa. Following IAT administration, focus groups explored user experience and elicited discussions of "good" versus "bad" patients and perceptions of male and female patients. IAT data were analyzed using Results: Eleven nurses participated and all completed the IAT, with 98% of trials retained after standard processing, indicating strong task engagement. Participants described the tool as understandable and usable, although some reported initial anxiety about responding "correctly." The mean Conclusions: This pilot study demonstrates that a locally developed gender-based IAT can be feasibly administered among public-sector health workers in South Africa. Although the quantitative signal was small and not statistically significant, qualitative findings revealed explicit gendered evaluative perceptions, supporting the instrument's internal face validity. This tool provides a contextually grounded approach to examining how gendered bias may shape health service delivery and establishes a foundation for larger-scale validation and implementation research.
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