Psychiatric Assessment of Social Impairment Across the Lifespan

Thom, Robyn P.; Keary, Christopher J.; Kramer, G. B.; Nowinski, Lisa A.; McDougle, Christopher J.

doi:10.1097/hrp.0000000000000257

Cited by 15 publications

(4 citation statements)

References 155 publications

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“…In its purest form, the nonspeaking behavior of children with SM and children with ASD is quite different. More specifically, children with SM display the prototypical muteness when exposed to socially demanding situations, but in comfortable situations their interaction with other people is normal, with a full range of emotion and adequate social cognition (Thom et al, 2020). In contrast, children with ASD display a more generalized impairment in social interaction: in both comfortable and uncomfortable circumstances, they exhibit impediments in social emotion, cognition, skills, and motivation (Pallathra et al, 2018), that may sometimes be expressed in non-speaking behavior.…”

Section: Introductionmentioning

confidence: 99%

Symptoms of Selective Mutism in Non-clinical 3- to 6-Year-Old Children: Relations With Social Anxiety, Autistic Features, and Behavioral Inhibition

et al. 2021

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Selective mutism (SM) is a psychiatric condition that is characterized by a failure to speak in specific social situations (e. g., at school) despite speaking normally in other situations (e.g., at home). There is abundant evidence that anxiety, and social anxiety in particular, is a prominent feature of SM, which is the main reason why this condition is currently classified as an anxiety disorder. Meanwhile, there is increasing support for the notion that autism-related problems are also involved in SM. The present study examined the relations between SM and social anxiety, autistic features, and behavioral inhibition to the unfamiliar (i.e., the tendency to react with restraint and withdrawal when confronted with unfamiliar stimuli and situations). Parents of 172 3- to 6-year-old preschool children completed an online survey for measuring the relevant constructs. Results showed that there were positive and statistically significant correlations between SM and social anxiety, autistic features, and behavioral inhibition. Regression analyses revealed that (1) both social anxiety and autistic features accounted for a significant and unique proportion of the variance in SM scores, and (2) that both of these variables no longer made a significant contribution once behavioral inhibition was added to the model. It can be concluded that while the involvement of social anxiety is unambiguous in SM, autism-related problems are also implicated. Furthermore, behavioral inhibition seems to play a key role in the non-speaking behavior of non-clinical young children.

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Section: Introductionmentioning

confidence: 99%

Symptoms of Selective Mutism in Non-clinical 3- to 6-Year-Old Children: Relations With Social Anxiety, Autistic Features, and Behavioral Inhibition

et al. 2021

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“…Adaptive social functioning has been related, among others, to healthier lifestyles [ 5 , 6 ], school engagement [ 7 ], and good interpersonal relationships [ 8 ]. By contrast, poor psychosocial functioning has been associated with school refusal [ 9 ], loneliness [ 10 ], emotion dysregulation [ 11 ], social anxiety and depression [ 12 , 13 ], psychotic experiences [ 14 ], psychotic disorders [ 15 – 17 ], as well as with suicidal risk [ 18 ]. Both psychosocial functioning and mental health problems are particularly relevant during adolescence and are regarded as potential predictors for future adverse outcomes in adulthood [ 9 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Initial validation of the Multidimensional Adolescent Functioning Scale (MAFS) in Spanish-speaking students from Chilean secondary schools

Núñez¹,

Villacura-Herrera²,

Gaete³

et al. 2023

Health Qual Life Outcomes

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Introduction Psychosocial functioning is closely associated with psychopathology and wellbeing in different populations, particularly adolescents. Despite its relevance, measures assessing psychosocial functioning in healthy adolescents are scant as most focus on adults or clinical populations. We evaluated the psychometric properties of the Multidimensional Adolescent Functioning Scale (MAFS), a self-report questionnaire created to assess three dimensions of psychosocial functioning (‘general functioning’, ‘family-related functioning’, and ‘peer-related functioning’) in adolescents from the general population. Methods After translation and cultural adaptation, we administered the Spanish MAFS to 619 adolescents aged 14 to 19. We assessed the factor structure, internal consistency, and associations with depressive symptoms, suicidal ideation, cognitive-behavioral skills, cognitive reappraisal (CR), and expressive suppression (ES). We additionally tested for measurement invariance based on biological sex. Results The original three-factor structure showed the best fit. Internal consistency was good for the total scale (ω = 0.874; α = 0.869; GLB = 0.939, rM=0.216) and for all subscales (ω = 0.806-0.839; α = 0.769 to 0.812; GLB = 0.861-0.873). Correlations between all three MAFS subscales were significant, ranging between 0.291 and 0.554. All MAFS subscales correlated positively and significantly with cognitive-behavioral skills and adaptive regulatory strategies and negatively with depressive symptoms and suicidal ideation. Conclusion The Spanish MAFS translation is a valid and reliable self-report measure to assess three domains of psychosocial functioning in adolescents aged 14–19 from the general population.

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“…Autism spectrum disorder (ASD) is an umbrella term for a set of highly heterogeneous neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted, repetitive behaviors (American Psychiatric Association, 2013). The estimated prevalence is ~1% of the population (Baxter et al., 2015) with a considerable amount of overlap with other disorders affecting social interaction (Hossain et al., 2020; Thom, Keary, Kramer, Nowinski, & McDougle, 2020). These characteristics call for efficient yet comprehensive diagnostic procedures.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co‐occurring diagnoses

Schulte‐Rüther

Kulvičius

Stroth

et al. 2022

Child Psychology Psychiatry

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Background: Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders. Method: We used a well-characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice. Results: We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses. Conclusions: ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.

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Psychiatric Assessment of Social Impairment Across the Lifespan

Cited by 15 publications

References 155 publications

Symptoms of Selective Mutism in Non-clinical 3- to 6-Year-Old Children: Relations With Social Anxiety, Autistic Features, and Behavioral Inhibition

Symptoms of Selective Mutism in Non-clinical 3- to 6-Year-Old Children: Relations With Social Anxiety, Autistic Features, and Behavioral Inhibition

Initial validation of the Multidimensional Adolescent Functioning Scale (MAFS) in Spanish-speaking students from Chilean secondary schools

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co‐occurring diagnoses

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