Examining the Intersections Between Medical Mistrust, Discrimination, and Shared Decision-Making Among Racially Diverse Women in the US: Insights From the 2024 Health Information National Trends Survey

Medical trust and shared decision-making (SDM) are crucial for delivering quality woman-centered care and are associated with improved health outcomes in various aspects of women’s health. However, longstanding structural and systemic barriers faced by racial and ethnic minority women result in lower baseline levels of trust, which makes engaging in meaningful and high-quality SDM more challenging. This study examined the relationship between medical mistrust and SDM among women and how this relationship differs among different racial groups. Data were pooled from the 2024 Health Information National Trends Survey. Weighted descriptive, bivariate, and multivariable logistic regression models with interaction terms were performed to assess the relationship between medical mistrust and SDM by race and ethnicity. Adjusted odds ratio (AOR), 95% confidence interval (CI), and the corresponding alpha of less than 0.05 were reported. Of the 4,107 women who responded to the HINTS survey, the final analytical sample comprised n = 3,923. Slightly over half of the participants (51.1%) reported high SDM engagement, while a substantial proportion (69.1%) experienced medical mistrust. Discrimination was reported by 20% of the sample. After adjusting for confounders, medical mistrust was significantly associated with reduced odds of SDM engagement (AOR 0.43; 95% CI: 0.30–0.62; p < 0.001) compared to those without medical mistrust. This association remained consistent in stratified models, where non-Hispanic White, non-Hispanic Black, Hispanic, and Asian/Other women all demonstrated a statistically significant reduction in the likelihood of SDM engagement in the presence of medical mistrust (p < 0.05). In the pooled sample, perceived discrimination reduced the odds of SDM (AOR 0.63; 95% CI: 0.44–0.91; p = 0.016). However, this relationship was inconsistent across groups. There was a statistically significant relationship between medical mistrust and SDM, with substantial differences across racial and ethnic groups. Findings show that medical mistrust, experience of discrimination, and perceived quality of care are strongly associated with SDM, with implications for woman-centered care. Improving the quality of the patient-provider relationship and woman-centered care—by fostering trust and eliminating discriminatory experiences—is crucial for increasing SDM, with health equity implications.

Authors

Institutions

Publication Details

Journal
Journal of Racial and Ethnic Health Disparities
Published
2026-03-27
DOI
https://doi.org/10.1007/s40615-026-02935-x
Primary Topic
Patient-Provider Communication in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Examining the Intersections Between Medical Mistrust, Discrimination, and Shared Decision-Making Among Racially Diverse Women in the US: Insights From the 2024 Health Information National Trends Survey

Kobi V. Ajayi
Journal of Racial and Ethnic Health Disparities
Patient-Provider Communication in Healthcare
article

Examining the Intersections Between Medical Mistrust, Discrimination, and Shared Decision-Making Among Racially Diverse Women in the US: Insights From the 2024 Health Information National Trends Survey

Kobi V. Ajayi
article en

Abstract

Medical trust and shared decision-making (SDM) are crucial for delivering quality woman-centered care and are associated with improved health outcomes in various aspects of women’s health. However, longstanding structural and systemic barriers faced by racial and ethnic minority women result in lower baseline levels of trust, which makes engaging in meaningful and high-quality SDM more challenging. This study examined the relationship between medical mistrust and SDM among women and how this relationship differs among different racial groups. Data were pooled from the 2024 Health Information National Trends Survey. Weighted descriptive, bivariate, and multivariable logistic regression models with interaction terms were performed to assess the relationship between medical mistrust and SDM by race and ethnicity. Adjusted odds ratio (AOR), 95% confidence interval (CI), and the corresponding alpha of less than 0.05 were reported. Of the 4,107 women who responded to the HINTS survey, the final analytical sample comprised n = 3,923. Slightly over half of the participants (51.1%) reported high SDM engagement, while a substantial proportion (69.1%) experienced medical mistrust. Discrimination was reported by 20% of the sample. After adjusting for confounders, medical mistrust was significantly associated with reduced odds of SDM engagement (AOR 0.43; 95% CI: 0.30–0.62; p < 0.001) compared to those without medical mistrust. This association remained consistent in stratified models, where non-Hispanic White, non-Hispanic Black, Hispanic, and Asian/Other women all demonstrated a statistically significant reduction in the likelihood of SDM engagement in the presence of medical mistrust (p < 0.05). In the pooled sample, perceived discrimination reduced the odds of SDM (AOR 0.63; 95% CI: 0.44–0.91; p = 0.016). However, this relationship was inconsistent across groups. There was a statistically significant relationship between medical mistrust and SDM, with substantial differences across racial and ethnic groups. Findings show that medical mistrust, experience of discrimination, and perceived quality of care are strongly associated with SDM, with implications for woman-centered care. Improving the quality of the patient-provider relationship and woman-centered care—by fostering trust and eliminating discriminatory experiences—is crucial for increasing SDM, with health equity implications.

Journal of Racial and Ethnic Health Disparities
Texas A&M Health Science Center (US), Texas A&M University (US)
Gender equality
Openalex Percentile: Top 8%
Patient-Provider Communication in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.