Behavioral Treatments for Anxiety in Adults With Autism Spectrum Disorder

White, Susan W.; Conner, Caitlin M.; Maddox, Brenna B.

doi:10.1016/b978-0-12-805122-1.00009-0

Cited by 3 publications

(1 citation statement)

References 80 publications

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“…In addition, it is notable that the autistic adults' stated preferences for therapy sessions fit nicely with CBT, which is a practical, present-focused, structured therapeutic approach with a growing evidence base for use in this population (Spain et al, 2015). The autistic participants' recommendations to consider sensory issues, slow the pace, incorporate special interests, use direct language, and set clear expectations align with published guidelines for modifying CBT for autistic adults (Kerns, Roux, Connell, & Shattuck, 2016;White, Conner, & Maddox, 2017;White et al, 2018).…”

Section: Discussionmentioning

confidence: 79%

“I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults

Maddox

Crabbe

Beidas

et al. 2019

Autism

121

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Most autistic adults struggle with mental health problems, and traditional mental health services generally do not meet their needs. This study used qualitative methods to identify ways to improve community mental health services for autistic adults for treatment of their co-occurring psychiatric conditions. We conducted semistructured, open-ended interviews with 22 autistic adults with mental healthcare experience, 44 community mental health clinicians, and 11 community mental health agency leaders in the United States. The participants identified clinician-, client-, and systems-level barriers and facilitators to providing quality mental healthcare to autistic adults. Across all three stakeholder groups, most of the reported barriers involved clinicians’ limited knowledge, lack of experience, poor competence, and low confidence working with autistic adults. All three groups also discussed the disconnect between the community mental health and developmental disabilities systems, which can result in autistic adults being turned away from services when they contact the mental health division and disclose their autism diagnosis during the intake process. Further efforts are needed to train clinicians to work more effectively with autistic adults and to increase coordination between the mental health and developmental disabilities systems. Lay Abstract Most autistic adults struggle with mental health problems, such as anxiety and depression. However, they often have trouble finding effective mental health treatment in their community. The goal of this study was to identify ways to improve community mental health services for autistic adults. We interviewed 22 autistic adults with mental healthcare experience, 44 community mental health clinicians (outpatient therapists, case managers, and intake coordinators), and 11 community mental health agency leaders in the United States. Our participants identified a variety of barriers to providing quality mental healthcare to autistic adults. Across all three groups, most of the reported barriers involved clinicians’ limited knowledge, lack of experience, poor competence, and low confidence working with autistic adults. All three groups also discussed the disconnect between the community mental health and developmental disabilities systems and the need to improve communication between these two systems. Further efforts are needed to train clinicians and provide follow-up consultation to work more effectively with autistic adults. A common suggestion from all three groups was to include autistic adults in creating and delivering the clinician training. The autistic participants provided concrete recommendations for clinicians, such as consider sensory issues, slow the pace, incorporate special interests, use direct language, and set clear expectations. Our findings also highlight a need for community education about co-occurring psychiatric conditions with autism and available treatments, in order to increase awareness about treatment options.

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Section: Discussionmentioning

confidence: 79%

“I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults

Maddox

Crabbe

Beidas

et al. 2019

Autism

121

View full text Add to dashboard Cite

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Aggregated Pattern Classification Method for improving neural disorder stage detection

Anjum,

Shahab,

Ahmad

et al. 2024

Brain and Behavior

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BackgroundNeurological disorders pose a significant health challenge, and their early detection is critical for effective treatment planning and prognosis. Traditional classification of neural disorders based on causes, symptoms, developmental stage, severity, and nervous system effects has limitations. Leveraging artificial intelligence (AI) and machine learning (ML) for pattern recognition provides a potent solution to address these challenges. Therefore, this study focuses on proposing an innovative approach—the Aggregated Pattern Classification Method (APCM)—for precise identification of neural disorder stages.MethodThe APCM was introduced to address prevalent issues in neural disorder detection, such as overfitting, robustness, and interoperability. This method utilizes aggregative patterns and classification learning functions to mitigate these challenges and enhance overall recognition accuracy, even in imbalanced data. The analysis involves neural images using observations from healthy individuals as a reference. Action response patterns from diverse inputs are mapped to identify similar features, establishing the disorder ratio. The stages are correlated based on available responses and associated neural data, with a preference for classification learning. This classification necessitates image and labeled data to prevent additional flaws in pattern recognition. Recognition and classification occur through multiple iterations, incorporating similar and diverse neural features. The learning process is finely tuned for minute classifications using labeled and unlabeled input data.ResultsThe proposed APCM demonstrates notable achievements, with high pattern recognition (15.03%) and controlled classification errors (CEs) (10.61% less). The method effectively addresses overfitting, robustness, and interoperability issues, showcasing its potential as a powerful tool for detecting neural disorders at different stages. The ability to handle imbalanced data contributes to the overall success of the algorithm.ConclusionThe APCM emerges as a promising and effective approach for identifying precise neural disorder stages. By leveraging AI and ML, the method successfully resolves key challenges in pattern recognition. The high pattern recognition and reduced CEs underscore the method's potential for clinical applications. However, it is essential to acknowledge the reliance on high‐quality neural image data, which may limit the generalizability of the approach. The proposed method allows future research to refine further and enhance its interpretability, providing valuable insights into neural disorder progression and underlying biological mechanisms.

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Mental Health Clinicians’ Screening and Intervention Practices to Reduce Suicide Risk in Autistic Adolescents and Adults

Jager‐Hyman

Maddox

Crabbe

et al. 2020

J Autism Dev Disord

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Behavioral Treatments for Anxiety in Adults With Autism Spectrum Disorder

Cited by 3 publications

References 80 publications

“I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults

“I wouldn’t know where to start”: Perspectives from clinicians, agency leaders, and autistic adults on improving community mental health services for autistic adults

Aggregated Pattern Classification Method for improving neural disorder stage detection

Mental Health Clinicians’ Screening and Intervention Practices to Reduce Suicide Risk in Autistic Adolescents and Adults

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