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Researchers and advocates have long called for improved research methods that better include people with intellectual and developmental disabilities (IDD), but challenges to doing so persist. Proxy responses are frequently used to circumvent some of these challenges, but may not fully capture the perspectives of people with IDD. The COVID-19 pandemic highlighted the possibility of consequences due to a lack of research about health outcomes for people with IDD, with documented disproportionate impact but little understanding about specific experiences for people with IDD. Using exploratory graph analysis (EGA) we explored the use of proxy responses on the COVID-19 Supplement of the National Core Indicators In-Person Survey. Findings suggest significant differences in response patterns between people who answered independently, via proxy, and with a mix of response types beyond what would be expected due to demographic differences in participants. [ABSTRACT FROM AUTHOR]

Having a sense of social inclusion and belonging, typically characterized by our personal relationships and community participation, is the central essence of life for most people, yet it remains elusive for many people with intellectual and developmental disabilities (IDD). This article summarizes the work of a diverse group of researchers and advocates to propose 6 big-picture, equity-based goals to drive future research in the field: (1) understanding the role of intersectionality, (2) understanding intimate relationships, (3) promoting formation of communities of care to support social inclusion, (4) understanding life course trajectories of social inclusion, (5) understanding social inclusion in virtual spaces, and (6) understanding how to promote social inclusion in the entire research process. [ABSTRACT FROM AUTHOR]

Objective: In the United States, people with intellectual and developmental disabilities (IDD) vote at significantly lower rates than people without disabilities and are underrepresented in the democratic process. This paper examines predictors of voting opportunity among people with IDD receiving state-funded disability services in Virginia. Method: We used secondary data from Virginia's 2017–2018 and 2018–2019 National Core Indicators In-Person Survey to conduct bivariate and regression analyses on possible predictors of voting opportunity identified from the literature. Results: Factors related to level of intellectual disability were significantly associated with having voting opportunity, as were several factors related to exercising other rights, including participating in the community and in self-advocacy training. Importantly, respondents who had participated in self-advocacy events were more likely to have voting opportunity and people with guardians were less likely to vote or to have had the opportunity. Conclusion: People with legal guardians may be the most likely to be excluded from the democratic process. Social workers and advocates seeking to increase voter turnout should strive to include people with more severe disabilities in self-advocacy events and other efforts to increase voting opportunity among people with IDD. [ABSTRACT FROM AUTHOR]

People with intellectual and developmental disabilities (IDD) may be at an increased risk of severe illness and death from COVID-19. This article examines the role of information and knowledge in COVID-19 vaccine uptake for people with IDD and their families. We developed a survey about COVID-19 vaccine uptake, confidence, and knowledge for people with IDD and their families. COVID-19 vaccine uptake was associated with higher self-reported knowledge about the vaccine, learning about the vaccine from one's doctor, and social media use. Qualitative results reflected the importance of trusted relationships with medical providers in vaccination. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]