Journal article
Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis
JMIR FORMATIVE RESEARCH, v 6(6), e33637
Jun 2022
PMID: 35275834
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Background: The prevalence of depression in the United States is >3 times higher mid-COVID-19 versus prepandemic. Racial/ethnic differences in mindsets around depression and the potential impact of the COVID-19 pandemic are not well characterized. Objective: This study aims to describe attitudes, mindsets, key drivers, and barriers related to depression pre- and mid-COVID-19 by race/ethnicity using digital conversations about depression mapped to health belief model (HBM) concepts. Methods: Advanced search, data extraction, and artificial intelligence-powered tools were used to harvest, mine, and structure open-source digital conversations of US adults who engaged in conversations about depression pre- (February 1, 2019-February 29, 2020) and mid-COVID-19 pandemic (March 1, 2020-November 1, 2020) across the internet. Natural language processing, text analytics, and social data mining were used to categorize conversations that included a self-identifier into racial/ethnic groups. Conversations were mapped to HBM concepts (ie, perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy). Results are descriptive in nature. Results: Of 2.9 and 1.3 million relevant digital conversations pre- and mid-COVID-19, race/ethnicity was determined among 1.8 million (62.2%) and 979,000 (75.3%) conversations, respectively. Pre-COVID-19, 1.3 million (72.1%) conversations about depression were analyzed among non-Hispanic Whites (NHW), 227,200 (12.6%) among Black Americans (BA), 189,200 (10.5%) among Hispanics, and 86,800 (4.8%) among Asian Americans (AS). Mid-COVID-19, a total of 736,100 (75.2%) conversations about depression were analyzed among NHW, 131,800 (13.5%) among BA, 78,300 (8.0%) among Hispanics, and 32,800 (3.3%) among AS. Conversations among all racial/ethnic groups had a negative tone, which increased pre- to mid-COVID-19; finding support from others was seen as a benefit among most groups. Hispanics had the highest rate of any racial/ethnic group of conversations showing an avoiding mindset toward their depression. Conversations related to external barriers to seeking treatment (eg, stigma, lack of support, and lack of resources) were generally more prevalent among Hispanics, BA, and AS than among NHW. Being able to benefit others and building a support system were key drivers to seeking help or treatment for all racial/ethnic groups. Conclusions: There were considerable racial/ethnic differences in drivers and barriers to seeking help and treatment for depression pre- and mid-COVID-19. As expected, COVID-19 has made conversations about depression more negative and with frequent
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Details
- Title
- Applying the Health Belief Model to Characterize Racial/Ethnic Differences in Digital Conversations Related to Depression Pre- and Mid-COVID-19: Descriptive Analysis
- Publication Details
- JMIR FORMATIVE RESEARCH, v 6(6), e33637
- Publisher
- JMIR PUBLICATIONS, INC; TORONTO
- Grant note
- This study was sponsored by Janssen Scientific Affairs, LLC, which had a role in the study design, analysis, interpretation of data; the writing of the paper; and the decision to publish. All authors were involved in the conception or design of the work and the acquisition, analysis, or interpretation of the data; critically reviewed the manuscript for content; and approved the final version to be published. Medical writing support was provided by Thomas J Parkman, PhD, MBA, of Cello Health Communications/MedErgy and was funded by Janssen Scientific Affairs, LLC.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000854080300070
- Scopus ID
- 2-s2.0-85128718272
- Other Identifier
- 991021861213804721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Medical Informatics