Assessment of Autism Spectrum Disorder

Yu, Yue; Ozonoff, Sally J; Miller, Meghan

doi:10.1177/10731911231173089

Cited by 7 publications

(3 citation statements)

References 134 publications

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“…Stronger ASD tendencies and a tendency to adopt an avoidant help-seeking style have been associated with poorer mental health. Poor communication and social skills are characteristics of ASD [47]. Their difficulty in effective communication and reluctance to seek advice or share their troubles with others, even when facing difficulties, suggests that they tend to keep their challenges to themselves.…”

Section: Discussionmentioning

confidence: 99%

A Sense of Belonging and Help Seeking: Examining Factors Related to the Mental Health of High School Students with High Autistic Traits without Diagnosis

Omiya,

Deguchi,

Asakura

2023

Children

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Certain individuals are clinically undiagnosed for Autism Spectrum Disorder (ASD) but exhibit strong ASD characteristics. This study examined the differences between a control group and a “high autistic traits” group involving individuals who scored 9 or higher on the Autism-Spectrum Quotient-16 Japanese Version, based on their sense of belonging, help-seeking style, and relationship with mental health. The participants were 608 Japanese public high school students. Of them, 129 (21.2%) and 479 (78.8%) were in the high autistic traits and control groups, respectively. A multiple regression analysis was performed using the General Health Questionnaire (GHQ) as the dependent variable for the groups. The analysis revealed no differences between the two groups regarding grade, gender, physical illness, insomnia, or mental health status; however, they differed regarding avoidant help-seeking style and teacher acceptance. Moreover, avoidant help-seeking scores in the high autistic traits group and teacher acceptance showed a significantly positive and significantly negative association with GHQ, respectively. The results indicated that children with autistic traits internally suppress them, experiencing distress. Teachers are aware that these students seek support from them, but the students seem reluctant to ask them for help. This can negatively impact the mental health of children with high autistic traits.

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Section: Discussionmentioning

confidence: 99%

A Sense of Belonging and Help Seeking: Examining Factors Related to the Mental Health of High School Students with High Autistic Traits without Diagnosis

Omiya,

Deguchi,

Asakura

2023

Children

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“…Although best practices exist, the process is far from perfect: Screening is often prone to false positives and diagnosis entails costly, interdisciplinary assessments (Levy et al, 2020;Yu et al, 2024). Hence, researchers must continue their search in identifying solutions to facilitate the timely diagnosis of autism.…”

Section: Recognize the Faces Of Autistic Childrenmentioning

confidence: 99%

Towards the Development of Explainable Machine Learning Models to Recognize the Faces of Autistic Children

Omrani,

Lanovaz,

Moroni

2024

Preprint

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Machine learning with image classification has shown promise in supporting the detection of autism in children. To extend this line of work, our study compared the use of two algorithms to explain why an image is categorized as autistic or not. First, we trained and tested models on the Autistic Children Facial Image Data Set to identify the three that produced the highest accuracy. Following the identification of the three best models, we used two methods to examine explainability: Local Interpretable Model-agnostic Explanations (LIME) and Randomized Input Sampling for Explanation of Black-box Models (RISE). Overall, the best model produced an accuracy of 92% and LIME resulted in more explainable models than RISE. Albeit promising, researchers must conduct more studies to examine the generalizability of the results prior to the adoption of this model in practice.

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“…Given these benefits, several self-report surveys and questionnaires have been developed to quantify autistic traits in adults, and these vary in length (i.e., number of items) and in the breadth of autistic trait dimensions captured. As the understanding and exploration of autism has evolved (Conner et al, 2019;Gunderson et al, 2023;Murray et al, 2005) and diagnostic criteria have been updated accordingly, many of the commonly used measures have become increasingly outdated (Agelink van Rentergem et al, 2019;Ashwood et al, 2016;Conner et al, 2019;Dorlack et al, 2018;Yu et al, 2023). For example, broad measures of autistic traits, such as the Broad Autism Phenotype Questionnaire (BAPQ; Hurley et al, 2007), Autism-Spectrum Quotient (Baron- Cohen et al, 2001), and Social Responsiveness Scale (Constantino et al, 2003), lack items relating to sensory sensitivity.…”

Section: Concerns With Established Measures Of Autistic Traitsmentioning

confidence: 99%

Psychometric evaluation of the Comprehensive Autistic Trait Inventory (CATI) in autistic and non-autistic adults

English,

Poulsen,

Maybery

et al. 2024

Preprint

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Autistic traits may be assessed for a variety of purposes including as a screener before seeking autism assessment, and as a measure of autistic-like traits as expressed in the general population for use in research. For such tools to be useful in research and clinical contexts, it is paramount that autistic trait inventories efficiently and accurately assess autism as it is currently defined and that they are valid in different demographic groups. The present study assesses the psychometric properties of the Comprehensive Autistic Trait Inventory (CATI), a relatively recently developed measure of autistic traits that examines six trait domains, including sensory sensitivity and social camouflage) using 42 self-report statements. To properly examine the CATI, we recruited large samples of autistic (n = 1,322) and non-autistic (n = 1,279) participants to complete the scale. Factor fit for the six subscale factor structure was appropriate as was total-scale and subscale reliability. Sensitivity and specificity supported the use of the CATI to discriminate autistic and non-autistic participants and total-scale classification threshold developed. Additional analyses examined differences between diagnosed (n = 737) and self-identifying (n = 585) autistic participants, with little to differentiate the groups. Gender differences for the subscales were noted across both autistic and non-autistic groups with a trend for higher scores to be observed in participants of a gender minority (n = 336) compared to cis-males (n = 1,205) and cis-females (n = 1,059), and gender-specific thresholds additionally computed. Lastly, the effect of age on autistic traits was examined with traits in non-autistic participants generally decreasing in older age groups and more complex pattern across the subscales noted for autistic participants. Overall, our results indicate that the CATI is a practical measure of autistic traits in both non-autistic and autistic participants and should be of use to researchers, clinicians, and the general public alike.

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Assessment of Autism Spectrum Disorder

Cited by 7 publications

References 134 publications

A Sense of Belonging and Help Seeking: Examining Factors Related to the Mental Health of High School Students with High Autistic Traits without Diagnosis

A Sense of Belonging and Help Seeking: Examining Factors Related to the Mental Health of High School Students with High Autistic Traits without Diagnosis

Towards the Development of Explainable Machine Learning Models to Recognize the Faces of Autistic Children

Psychometric evaluation of the Comprehensive Autistic Trait Inventory (CATI) in autistic and non-autistic adults

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