# Prediction of therapist cultural competency using Natural Language Processing (NLP) models

> **NIH NIH F31** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2021 · $46,036

## Abstract

PROJECT SUMMARY
 Racial-ethnic minorities (REM) and lesbian, gay, bisexual, transgender, and queer (LGBTQ) individuals
experience high levels of psychological distress. Psychological treatments can be effective in addressing
mental health concerns, but disparities in quality of care still exist. Although systemic and institutional factors
contribute to disparities in care, mental health providers are also critical to examine. A primary focus of efforts
to understand and reduce provider contributions to mental health care disparities has been to examine cultural
competency (CC), which involves a provider’s ability to navigate the cultural aspects of clinical interactions.
Patient ratings of CC are generally associated with treatment outcomes and therapeutic processes. While
patient perceptions of provider CC are important, a reliance on retrospective patient ratings limits what we
know about how cultural identities are discussed, and the language that constitutes culturally sensitive care.
Many studies of provider CC also require observers or patients to make complex judgments based on internal
provider characteristics that are not reliably observable (e.g. rate provider awareness of their own cultural
values). More studies are needed that examine patient-provider interactions in treatment in order to assess the
impact of specific provider behaviors, and how they relate to perceptions of provider CC. Recently, Natural
Language Processing (NLP) models have been applied to psychotherapy conversations to automatically
capture the use of evidence based treatments, topics of conversation, empathy, and emotional expression.
Prior research demonstrating the feasibility of automatically identifying topics of conversation in psychotherapy
suggest that NLP models could be trained to automatically identify specific moments in sessions where
patients and providers are talking about cultural issues. NLP models could allow researchers to not only
examine how specific patterns of provider-patient interactions drive CC, but might also provide rapid feedback
to providers, and in turn help address disparities in care. The purpose of the current study is to do the
foundational work to develop and evaluate NLP tools that capture the cultural content of provider-patient
interactions among REM and LGBTQ patients. First, utilizing 32,436 labeled talk turns from 200 psychotherapy
sessions we will evaluate the accuracy of NLP models in recognizing the discussion of cultural topics in
psychotherapy. Second, we will use NLP models to explore differences in the content of 1,235 psychotherapy
sessions that were rated as highly positive or negative on a measure of cultural competence.

## Key facts

- **NIH application ID:** 10126722
- **Project number:** 5F31MD014941-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Patty Beyrong Kuo
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 5
- **Project period:** 2020-02-12 → 2021-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10126722

## Citation

> US National Institutes of Health, RePORTER application 10126722, Prediction of therapist cultural competency using Natural Language Processing (NLP) models (5F31MD014941-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10126722. Licensed CC0.

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