Smartphone-Based Virtual and Augmented Reality Implicit Association Training (VARIAT) for Reducing Implicit Biases Toward Patients Among Health Care Providers: App Development and Pilot Testing.
Jiabin Shen, Alex J Clinton, Jeffrey Penka, Megan E Gregory, Lindsey Sova, Sheryl Pfeil, Jeremy Patterson, Tensing Maa
{"title":"Smartphone-Based Virtual and Augmented Reality Implicit Association Training (VARIAT) for Reducing Implicit Biases Toward Patients Among Health Care Providers: App Development and Pilot Testing.","authors":"Jiabin Shen, Alex J Clinton, Jeffrey Penka, Megan E Gregory, Lindsey Sova, Sheryl Pfeil, Jeremy Patterson, Tensing Maa","doi":"10.2196/51310","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Implicit bias is as prevalent among health care professionals as among the wider population and is significantly associated with lower health care quality.</p><p><strong>Objective: </strong>The study goal was to develop and evaluate the preliminary efficacy of an innovative mobile app, VARIAT (Virtual and Augmented Reality Implicit Association Training), to reduce implicit biases among Medicaid providers.</p><p><strong>Methods: </strong>An interdisciplinary team developed 2 interactive case-based training modules for Medicaid providers focused on implicit bias related to race and socioeconomic status (SES) and sexual orientation and gender identity (SOGI), respectively. The simulations combine experiential learning, facilitated debriefing, and game-based educational strategies. Medicaid providers (n=18) participated in this pilot study. Outcomes were measured on 3 domains: training reactions, affective knowledge, and skill-based knowledge related to implicit biases in race/SES or SOGI.</p><p><strong>Results: </strong>Participants reported high relevance of training to their job for both the race/SES module (mean score 4.75, SD 0.45) and SOGI module (mean score 4.67, SD 0.50). Significant improvement in skill-based knowledge for minimizing health disparities for lesbian, gay, bisexual, transgender, and queer patients was found after training (Cohen d=0.72; 95% CI -1.38 to -0.04).</p><p><strong>Conclusions: </strong>This study developed an innovative smartphone-based implicit bias training program for Medicaid providers and conducted a pilot evaluation on the user experience and preliminary efficacy. Preliminary evidence showed positive satisfaction and preliminary efficacy of the intervention.</p>","PeriodicalId":14795,"journal":{"name":"JMIR Serious Games","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11004623/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Serious Games","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/51310","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Implicit bias is as prevalent among health care professionals as among the wider population and is significantly associated with lower health care quality.
Objective: The study goal was to develop and evaluate the preliminary efficacy of an innovative mobile app, VARIAT (Virtual and Augmented Reality Implicit Association Training), to reduce implicit biases among Medicaid providers.
Methods: An interdisciplinary team developed 2 interactive case-based training modules for Medicaid providers focused on implicit bias related to race and socioeconomic status (SES) and sexual orientation and gender identity (SOGI), respectively. The simulations combine experiential learning, facilitated debriefing, and game-based educational strategies. Medicaid providers (n=18) participated in this pilot study. Outcomes were measured on 3 domains: training reactions, affective knowledge, and skill-based knowledge related to implicit biases in race/SES or SOGI.
Results: Participants reported high relevance of training to their job for both the race/SES module (mean score 4.75, SD 0.45) and SOGI module (mean score 4.67, SD 0.50). Significant improvement in skill-based knowledge for minimizing health disparities for lesbian, gay, bisexual, transgender, and queer patients was found after training (Cohen d=0.72; 95% CI -1.38 to -0.04).
Conclusions: This study developed an innovative smartphone-based implicit bias training program for Medicaid providers and conducted a pilot evaluation on the user experience and preliminary efficacy. Preliminary evidence showed positive satisfaction and preliminary efficacy of the intervention.
期刊介绍:
JMIR Serious Games (JSG, ISSN 2291-9279) is a sister journal of the Journal of Medical Internet Research (JMIR), one of the most cited journals in health informatics (Impact Factor 2016: 5.175). JSG has a projected impact factor (2016) of 3.32. JSG is a multidisciplinary journal devoted to computer/web/mobile applications that incorporate elements of gaming to solve serious problems such as health education/promotion, teaching and education, or social change.The journal also considers commentary and research in the fields of video games violence and video games addiction.