scholarly journals Optimizing Coaching During Online Relationship Education for Low-Income Couples: A Precision Medicine Research Protocol (Preprint)

10.2196/33047 ◽  
2021 ◽  
Author(s):  
S. Gabe Hatch ◽  
Diana Lobaina ◽  
Brian D. Doss
Keyword(s):  
Precision Medicine ◽  
Low Income ◽  
Relationship Education ◽  
Research Protocol ◽  
Medicine Research ◽  
Precision Medicine Research

Optimizing Coaching During Online Relationship Education for Low-Income Couples: A Precision Medicine Research Protocol (Preprint)

2021 ◽  
Author(s):  
S. Gabe Hatch ◽  
Diana Lobaina ◽  
Brian D. Doss
Keyword(s):  
Relationship Satisfaction ◽  
Social Services ◽  
Treatment Adherence ◽  
Low Income ◽  
Relationship Education ◽  
Attrition Rates ◽  
Medicine Research ◽  
Direct Benefits ◽  
Effective Delivery ◽  
Precision Medicine Research

BACKGROUND In-person relationship education classes funded by the federal government tend to experience relatively high attrition rates and have only limited effects on relationships. In contrast, low-income couples tend to report meaningful gains from online relationship education when provided with individualized coach contact. However, little is known about the method and intensity of practitioner contact that a couple requires to complete the online program and receive the intended benefit. OBJECTIVE The current protocol seeks to: a) use the within-group models to create an algorithm to assign future couples to different programs and level of coach contact, b) identify the most powerful predictors of treatment adherence and gains in relationship satisfaction within three different levels of coaching, and c) examine the most powerful predictors of treatment adherence and gains in relationship satisfaction between three levels of coach contact. METHODS To accomplish these goals, this project intends to use data from an online Sequential Multiple Assignment Randomized Trial of the OurRelationship and ePREP programs where the method and type of coach contact were randomly varied across 1,248 couples (2,496 individuals) with the hope of advancing theory in this area and generating accurate predictions. RESULTS The current protocol was funded by the U.S. Department of Health and Human Services, Administration for Children and Families, Grant Number 90PD0309. CONCLUSIONS Some of the direct benefits from this protocol include benefits to social services programs administrators, tailoring of more effective relationship education, and the effective delivery of evidence- and web-based relationship health interventions. CLINICALTRIAL The current protocol was pre-registered with ClinicalTrials.gov (NCT02806635).

Increasing inclusivity in precision medicine research: Views of individuals who are deaf and hard of hearing

2021 ◽  
Author(s):  
Diana C. Garofalo ◽  
Howard A. Rosenblum ◽  
Yuan Zhang ◽  
Ying Chen ◽  
Paul S. Appelbaum ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Hard Of Hearing ◽  
Medicine Research ◽  
Precision Medicine Research

scholarly journals Machine Learning Methods for Precision Medicine Research Designed to Reduce Health Disparities: A Structured Tutorial

2020 ◽  
Vol 30 (Suppl 1) ◽  
pp. 217-228 ◽  
Author(s):  
Sanjay Basu ◽  
James H. Faghmous ◽  
Patrick Doupe
Keyword(s):  
Machine Learning ◽  
Health Disparities ◽  
Precision Medicine ◽  
Learning Approaches ◽  
Learning Methods ◽  
Medicine Research ◽  
Machine Learning Methods ◽  
Box Models ◽  
Precision Medicine Research ◽  
Key Terms

  Precision medicine research designed to reduce health disparities often involves studying multi-level datasets to understand how diseases manifest disproportionately in one group over another, and how scarce health care resources can be directed precisely to those most at risk for disease. In this article, we provide a structured tutorial for medical and public health research­ers on the application of machine learning methods to conduct precision medicine research designed to reduce health dispari­ties. We review key terms and concepts for understanding machine learning papers, including supervised and unsupervised learning, regularization, cross-validation, bagging, and boosting. Metrics are reviewed for evaluating machine learners and major families of learning approaches, including tree-based learning, deep learning, and ensemble learning. We highlight the advan­tages and disadvantages of different learning approaches, describe strategies for interpret­ing “black box” models, and demonstrate the application of common methods in an example dataset with open-source statistical code in R.Ethn Dis. 2020;30(Suppl 1):217-228; doi:10.18865/ed.30.S1.217

scholarly journals Disability inclusion in precision medicine research: a first national survey

2019 ◽  
Vol 21 (10) ◽  
pp. 2319-2327 ◽  
Author(s):  
Maya Sabatello ◽  
Ying Chen ◽  
Yuan Zhang ◽  
Paul S. Appelbaum
Keyword(s):  
Precision Medicine ◽  
National Survey ◽  
Medicine Research ◽  
Precision Medicine Research

scholarly journals Conducting Precision Medicine Research with African Americans

PLoS ONE ◽  
2016 ◽  
Vol 11 (7) ◽  
pp. e0154850 ◽  
Author(s):  
Chanita Hughes Halbert ◽  
Jasmine McDonald ◽  
Susan Vadaparampil ◽  
LaShanta Rice ◽  
Melanie Jefferson
Keyword(s):  
African Americans ◽  
Precision Medicine ◽  
Medicine Research ◽  
Precision Medicine Research
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