An Examination of Support Needs, Supports, and Outcomes for People With Intellectual and Developmental Disabilities.

Researchers used a merged dataset to examine if more resources were expended on those with greater support needs and if support needs impacted personal outcomes when controlling for relevant personal and contextual factors. Results indicated that the amount of support a person receives had a direct relationship to their needs. However, we also found that people with the greatest needs had weaker personal outcomes suggesting that distribution of resources based on need may not result in equivalent outcomes. The authors suggest strategies at an individual and systems level to address the outcomes gap for people with the greatest support needs.

Measuring Four Personal Opportunities for Adults With Intellectual and Developmental Disabilities.

This study tests an empirically derived model for measuring personal opportunities for people with intellectual and developmental disabilities (IDD) using National Core Indicators In-Person Survey (NCI-IPS) state and national datasets. The four personal opportunities measured, (a) privacy rights, (b) everyday choice, (c) community participation, and (d) expanded friendships, were informed by existing conceptualizations of service as well as NCI-IPS measures. Analyses confirmed the fit of a four-factor model and demonstrated that factors were significantly and positively correlated. To demonstrate the relationships between personal opportunities and personal and environmental characteristics, we estimated a structural equation model that regressed personal opportunities on age, gender, place of residence, and level of intellectual disability. Implications for using personal opportunities for evaluating service quality of IDD systems are discussed.

Using Machine Learning to Predict Patterns of Employment and Day Program Participation.

In this article, we demonstrate the potential of machine learning approaches as inductive analytic tools for expanding our current evidence base for policy making and practice that affects people with intellectual and developmental disabilities (IDD). Using data from the National Core Indicators In-Person Survey (NCI-IPS), a nationally validated annual survey of more than 20,000 nationally representative people with IDD, we fit a series of classification tree and random forest models to predict individuals' employment status and day activity participation as a function of their responses to all other items on the 2017-2018 NCI-IPS. The most accurate model, a random forest classifier, predicted employment outcomes of adults with IDD with an accuracy of 89 percent on the testing sample, and 80 percent on the holdout sample. The most important variable in this prediction was whether or not community employment was a goal in this person's service plan. These results suggest the potential machine learning tools to examine other valued outcomes used in evidence-based policy making to support people with IDD.

Measuring and Tracking Personal Opportunity Outcome Measures Over 3 Years to Guide Policy and Services That Promote Inclusive Community Living.

National policy and litigation have been a catalyst in many states for expanding personal outcomes for people with intellectual and developmental disabilities (IDD) and have served as an impetus for change in state IDD systems. Although several metrics are used to examine personal outcomes, the National Core Indicators (NCI) In-Person Survey (IPS) is one tool that provides an annual depiction of the lives of people who receive Medicaid Home and Community Based IDD waiver services (HCBS). This article examines whether a validated, three-factor (Privacy Rights, Everyday Choice, and Community Participation) measure of Personal Opportunity, derived from NCI items, functions as predicted across non-equivalent, NCI cohorts (N=2400) from Virginia in 2017, 2018, and 2019. Multiple-groups confirmatory factor analysis (CFA) was employed to examine the invariance and generalizability of the Personal Opportunity constructs. Results indicated that Privacy Rights, Everyday Choice, and Community Participation measured the same concepts even when time and group varied. Significant improvements in Privacy Rights and Community Participation were observed when comparing latent factor means across years. Findings provide stakeholders with a tool for interpreting personal outcomes in the contexts of policy and practice intended to improve inclusion and quality of life for adults with IDD.

Studying abroad inclusively: Reflections by college students with and without intellectual disability.

Postsecondary education programs have increased opportunities for students with and without intellectual disabilities to study abroad as inclusive classes. Using open-coding qualitative techniques, the authors examined an inclusive study abroad group's daily reflective journals during a study abroad trip to London and Dublin. Three shared categories emerged from analysis: personal development, bonding/social inclusion, and learning from English and Irish adults with intellectual disabilities. Each group reported two distinct categories as well. Students with intellectual disabilities described the importance of mobility/transportation and fun, while their classmates without intellectual disabilities described the importance of inclusive learning and an increasing awareness of barriers to full participation for people with disabilities. Student-constructed categories are used to describe the benefits of inclusive study abroad and build future inclusive international opportunities.