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Autistic people experience disparities in employment which may be exacerbated for individuals in skilled employment. Little is known about the experiences of autistic people in skilled employment or how they define success. We used a community-based participatory research approach to conduct a thematic analysis with an inductive approach at a semantic level through a critical realist paradigm. We interviewed 45 autistic people with skilled training, 11 supervisors, and 8 key informants. We purposively sampled to maximize variation. We addressed trustworthiness through multiple coders and peer debriefing. Common themes included high stakes of disclosure, unconventional pathways to careers, disconnects with service and support systems, mental health challenges from trauma/burnout, autistic advantages in the workplace, and complex dimensions of discrimination. Participants defined success as opportunities for growth, work/life balance, financial independence, sense of community, and feeling valued, accepted, and like their work had meaning. Strategies to facilitate success suggested a multi-faceted and wholistic approach including attention to the role of supervisors. Our findings suggest a highly customizable, systems-focused, multifaceted approach to autism employment intervention could be useful in improving skilled employment outcomes. We recommend further work particularly in the areas of disclosure and destigmatizing disability in the workplace.

Computer-based algorithms have the potential to encode and exacerbate ableism and may contribute to disparate outcomes for disabled people. The threat of algorithmic bias to people with disabilities is inseparable from the longstanding role of technology as a normalizing agent, and from questions of how society defines shared values, quantifies ethics, conceptualizes and measures risk, and strives to allocate limited resources. This article situates algorithmic bias amidst the larger context of normalization, draws on social and critical theories that can be used to better understand both ableism and algorithmic bias as they operate in the United States, and proposes concrete steps to mitigate harm to the disability community as a result of algorithmic adoption. Examination of two cases -- the allocation of lifesaving medical interventions during the COVID-19 pandemic and approaches to autism diagnosis and intervention -- demonstrate instances of the mismatch between disabled people's lived experiences and the goals and understandings advanced by nondisabled people. These examples highlight the ways particular ethical norms can become part of technological systems, and the harm that can ripple outward from misalignment of formal ethics and community values. [ABSTRACT FROM AUTHOR]

Definitions of disability have evolved over time. Consistent with the biopsychosocial model used by the World Health Organization, we conceptualize disability as an interaction between a person's functional impairments or chronic health conditions and the physical and social environment. Having a disability is not synonymous with poor health, and maintaining and improving health is equally important for both people with and people without disabilities. In this article we review estimates of disability prevalence in the US and present evidence of differences in prevalence by race, ethnicity, and sexual orientation; health disparities by disability status and type of disability; and health disparities for people whose disability intersects with other forms of marginalization. We suggest policy changes to advance equity, reduce disparities, and enhance the health and well-being of all Americans with disabilities. [ABSTRACT FROM AUTHOR]