Implicit organizational bias: Mental health treatment culture and norms as barriers to engaging with diversity

Am Psychol. 2021 Jan;76(1):78-90. doi: 10.1037/amp0000621. Epub 2020 Mar 5.


There are increased efforts to improve patient-provider relations and engagement within North American mental health systems. However, it is unclear how these innovations impact care for ethnic minorities, a group that continues to face social and health disparities. This study examined one such engagement innovation-person-centered care planning-to gain a better understanding of this overall process. We specifically explored how mental health providers trained in person-centered care planning work with their patients of Latinx and Asian backgrounds. In-depth interviews were conducted with mental health providers in community clinics, and narratives were analyzed via phenomenological methods. Findings revealed that regardless of specific practice innovations, it was providers' own embeddedness in their mental health organizational culture that became conspicuous as a determinant of care. This culture contained implicit preferences for clients considered to be ideal (e.g., are verbal, admit a problem or illness, accept services, and are individually oriented). These clients were experienced as ideal largely because they helped the system operate efficiently. Findings suggest that these organizational norms, preferences, and expectations-and bureaucratic demands for efficiency-may engender an implicit organizational bias that creates barriers for culturally different groups. These biases may also hinder practice innovations, whether patient-centered, disparities-focused, or otherwise. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Ethnicity / statistics & numerical data
  • Female
  • Healthcare Disparities*
  • Humans
  • Male
  • Mental Health Services / organization & administration*
  • Mental Health*
  • Minority Groups / statistics & numerical data
  • Prejudice*