Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach

Medical distrust is a potent barrier to participation in HIV care and medication use among African American/Black and Latino (AABL) persons living with HIV (PLWH). However, little is known about sociodemographic and risk factors associated with distrust. We recruited adult AABL PLWH from low socio-e...

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Autores principales:	Ning He, Charles M. Cleland, Marya Gwadz, Dawa Sherpa, Amanda S. Ritchie, Belkis Y. Martinez, Linda M. Collins
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
Acceso en línea:	https://doaj.org/article/18aa5f2cf7ea41c79682d82553079400
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spelling	oai:doaj.org-article:18aa5f2cf7ea41c79682d825530794002021-12-02T08:03:36ZUnderstanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach2158-244010.1177/21582440211061314https://doaj.org/article/18aa5f2cf7ea41c79682d825530794002021-12-01T00:00:00Zhttps://doi.org/10.1177/21582440211061314https://doaj.org/toc/2158-2440Medical distrust is a potent barrier to participation in HIV care and medication use among African American/Black and Latino (AABL) persons living with HIV (PLWH). However, little is known about sociodemographic and risk factors associated with distrust. We recruited adult AABL PLWH from low socio-economic status backgrounds with insufficient engagement in HIV care ( N = 512). Participants completed structured assessments on three types of distrust (of health care providers, health care systems, and counter-narratives), HIV history, and mental health. We used a type of machine learning called random forest to explore predictors of trust. On average, participants were 47 years old ( SD = 11 years), diagnosed with HIV 18 years prior ( SD = 9 years), and mainly male (64%) and African American/Black (69%). Depression and age were the most important predictors of trust. Among those with elevated depressive symptoms, younger participants had less trust than older, while among those without depression, trust was greater across all ages. The present study adds nuance to the literature on medical distrust among AABL PLWH and identifies junctures where interventions to build trust are needed most.Ning HeCharles M. ClelandMarya GwadzDawa SherpaAmanda S. RitchieBelkis Y. MartinezLinda M. CollinsSAGE PublishingarticleHistory of scholarship and learning. The humanitiesAZ20-999Social SciencesHENSAGE Open, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
spellingShingle	History of scholarship and learning. The humanities AZ20-999 Social Sciences H Ning He Charles M. Cleland Marya Gwadz Dawa Sherpa Amanda S. Ritchie Belkis Y. Martinez Linda M. Collins Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
description	Medical distrust is a potent barrier to participation in HIV care and medication use among African American/Black and Latino (AABL) persons living with HIV (PLWH). However, little is known about sociodemographic and risk factors associated with distrust. We recruited adult AABL PLWH from low socio-economic status backgrounds with insufficient engagement in HIV care ( N = 512). Participants completed structured assessments on three types of distrust (of health care providers, health care systems, and counter-narratives), HIV history, and mental health. We used a type of machine learning called random forest to explore predictors of trust. On average, participants were 47 years old ( SD = 11 years), diagnosed with HIV 18 years prior ( SD = 9 years), and mainly male (64%) and African American/Black (69%). Depression and age were the most important predictors of trust. Among those with elevated depressive symptoms, younger participants had less trust than older, while among those without depression, trust was greater across all ages. The present study adds nuance to the literature on medical distrust among AABL PLWH and identifies junctures where interventions to build trust are needed most.
format	article
author	Ning He Charles M. Cleland Marya Gwadz Dawa Sherpa Amanda S. Ritchie Belkis Y. Martinez Linda M. Collins
author_facet	Ning He Charles M. Cleland Marya Gwadz Dawa Sherpa Amanda S. Ritchie Belkis Y. Martinez Linda M. Collins
author_sort	Ning He
title	Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
title_short	Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
title_full	Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
title_fullStr	Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
title_full_unstemmed	Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach
title_sort	understanding medical distrust among african american/black and latino persons living with hiv with sub-optimal engagement along the hiv care continuum: a machine learning approach
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/18aa5f2cf7ea41c79682d82553079400
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Understanding Medical Distrust Among African American/Black and Latino Persons Living With HIV With Sub-Optimal Engagement Along the HIV Care Continuum: A Machine Learning Approach

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