# Increasing health care equity by examining a possible mediator of the relationship between implicit bias and provider behavior: intergroup anxiety

> **NIH AHRQ R36** · UNIVERSITY OF WASHINGTON · 2021 · $20,941

## Abstract

Project summary. Health disparities are a matter of grave public health significance1, and producing
evidence to make health care more equitable is part of the Agency for Health Care Research and Quality
(AHRQ) mission. Racial health disparities have complex etiologies and correlates, but remain when controlling
for other social determinants of health2 and patient factors (e.g. treatment refusal3). Residual disparities reflect
differences in provider treatment of White and minority patientse.g.4–6. One factor contributing to disparate
treatment of minority patients is provider implicit bias—non-conscious biases that alter behavior7. Provider
implicit bias predicts subtle behavioral differences in interactions with minority patients, including more anxiety-
related words8, more negative affect9, and different nonverbals10. These provider behaviors predict lower
patient satisfaction and adherence11, with large health consequences11,12. Despite haste to target implicit bias
in disparity reduction interventions, most studies show no impact of implicit bias interventions19. Further,
implicit bias is difficult to measure and demonstrates moderate test-retest reliability13. Still, there are public
health implications of even weak effects of implicit bias14 when considering the number of people affected.
 Effects of implicit bias on disparities may be clarified by articulating and examining more complex models
of the relationship between implicit bias and provider behavior. This proposal examines intergroup anxiety
(anxiety that manifests in interracial interactions in response to negative expectations15) as a mediator of the
relationship between implicit bias and provider behavior. It is well known that anxiety affects behavior in the
general populatione.g.16, and provider anxiety impairs patient outcomes, such as satisfaction and adherence17,18,
but no research has examined the effects of intergroup anxiety on provider behavior. We will test this model in
a sample (N=70) of medical students. Participants will each interact with two patient actors—one Black, and
one White—to control for race-irrelevant anxiety. To ensure a comprehensive analysis of the innovative
association between intergroup anxiety and provider behavior, we propose to measure both constructs at
multiple levels. We will assess anxiety through self-report affect and physiology19. We will examine three
classes of behavior: verbal (anxiety-related word use), global (warmth), and nonverbal (smiling and eye
contact). Medical school is a key window-of-opportunity when biases may be more malleable20, students are
accessible, and training is expected. Many medical schools use implicit bias reduction trainings to decrease
disparities, but intergroup anxiety may represent a more consistently alterable and easy-to-measure construct.
Disparity-reduction trainings based on evidence-based models such as the proposed may have large impacts
on health disparities, addressing the AHRQ priority focus of...

## Key facts

- **NIH application ID:** 10335089
- **Project number:** 1R36HS027743-01A1
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Katherine E Manbeck
- **Activity code:** R36 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $20,941
- **Award type:** 1
- **Project period:** 2021-09-30 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10335089

## Citation

> US National Institutes of Health, RePORTER application 10335089, Increasing health care equity by examining a possible mediator of the relationship between implicit bias and provider behavior: intergroup anxiety (1R36HS027743-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10335089. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*