Developmental Psychology and Healthcare Professions: Autism Knowledge Among Nurses: An Observational Study.

One of the biggest limitations faced by autistic people is the lack of knowledge of their condition. Our study aims to evaluate and discuss the knowledge of autism among nurses, which is a social and health category often in close contact with autistic people. Given the limited exploration of awareness levels about autism among healthcare professionals, this study aims to investigate general and specific knowledge of autism within a group of nursing students enrolled in a master's degree.

Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.

In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain features that previous literature has identified as prominent in the classification process. Our study employs a game-based tablet application to collect motor data.

Pregnancy in autistic women and social medical considerations: scoping review and meta- synthesis.

Introduction: This article addresses a topic that has been largely overlooked by scientific literature, namely pregnancy in autistic women. Generally, the issue of sexuality in disability, particularly in disabled women, autistic or otherwise, has been underexplored. However, it is necessary to scientifically investigate this topic to propose adequate social and health policies.

A deep learning latent variable model to identify children with autism through motor abnormalities.

Introduction: Autism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialists' collaborative efforts to be properly accomplished. Most recent research has been conducted on automated ASD detection using advanced technologies.

Examining Predictors of Different ABA Treatments: A Systematic Review.

In the recent literature, there is a broad consensus on the effectiveness of Applied Behavior Analysis interventions for autism spectrum disorder (ASD). Despite their proven efficacy, research in clinical settings shows that these treatments are not equally effective for all children and the issue of which intervention should be chosen for an individual remains a common dilemma. The current work systematically reviewed studies on predictors and moderators of response to different types of evidence-based treatment for children with ASD.

Age-Related Differences in Psychological Distress during the COVID-19 Pandemic.

While the negative impact of COVID-19 total lockdown on mental health in youth has been extensively studied, findings collected during subsequent waves of the pandemic, in which restrictive rules were more eased, are very sparse. Here, we explore perceived psychological distress during the partial lockdown of the third wave in Southern Italy in a large sample of students, focusing on age and gender differences. Also, we assessed whether attending the type of education could have a protective role on students' psychological well-being.

Using Technology to Identify Children With Autism Through Motor Abnormalities.

Autism is a neurodevelopmental disorder typically assessed and diagnosed through observational analysis of behavior. Assessment exclusively based on behavioral observation sessions requires a lot of time for the diagnosis. In recent years, there is a growing need to make assessment processes more motivating and capable to provide objective measures of the disorder.