ASD Support Networks and the Services Cliff: Mappings Social Capital Inequalities for Young Adults with ASD Post-Transition

Background: Over 66% of young adults with Autism Spectrum Disorder (ASD) are disconnected from opportunities for work or schooling in the first few years after high school. ¹ Unequal access to social capital (resources and connections) from work, community and school contexts could adversely impact their transition to employment and other lifecourse outcomes. ^2-4 Our study represents the first attempt to use modern social network methods to investigate the social networks of transition-age youth with autism to investigate social capital inequalities during transition.

Objectives: To conduct a preliminary investigation of the social capital of youth with ASD post transition by using pilot data from self-report egocentric social network surveys to identify resources and connections and investigate variation across gender.

Methods: A purposeful sample of 17 young adults between the age of 19-28 were asked to identify up to five important people and the supports they provided to the young adult. The young adults also identified network connections among identified supporters. Using ORA ⁵, we computed the density of the team of identified supporters, the percent for each role type and the percent for each support type of supporter. Descriptive statistics suggest that participants represented a racially homogenous (88% white), relatively advantaged group of 17 young adults, with a mean age 23.25. Overall, 82% had ever attended college and 35% were living independently. 59% were male (n=10), 29% female (n=5), and 12% were gender non-conforming (n=2) (Table 1).

Results: Pilot data suggest that youth were not isolated, with mean networks size = 4.88. Few support networks included professionals (n=2). Types of supports provided by network members varied. Friendship (75%), emotional support (74%) and advice (73%) were more frequent while employment support was less frequent (24%). Median network density for males was 100%, while female or gender non-conforming persons median density was 60% (Mann–Whitney U = 13, p = .03, two-tailed). (Figure 1) Role types of supporters varied by gender. Family members were marginally more present in males’ support networks (median 68%) than female and gender non-conforming persons’ (median 40%; Mann–Whitney U = 17.5, p = .09, two-tailed). In contrast, community members were more present in the support networks of females and gender non-conforming persons (median 40%) than in the support networks of males (median 12.5%) (Mann–Whitney U = 53, p = .08, two-tailed). Family friendship also varied by gender, where males had on average a marginally greater percentage of family members who provided friendship (median 60%) than females and gender non-conforming persons (median 40%) (Mann–Whitney U = 15.5, p = .06, two-tailed).

Conclusions: Pilot data suggests that youth were not isolated and that gender is salient for diverse sources of social capital acquisition, as male young adults with ASD in this study relied primarily on family network members, while females and gender non-conforming persons had more diverse networks that included family and community members. The lack of professionals present in support networks illustrates the adverse impact of the service cliff, as identified in previous studies.¹