Work-Related Training for Individuals With Autism: Does It Work?

2021 ◽  
Vol 75 (Supplement_2) ◽  
pp. 7512515300p1-7512515300p1
Author(s):  
Marie-Christine Potvin ◽  
Emily Anderson ◽  
Lindsey Hatton ◽  
Mary Ferarro ◽  
Elyse C. Kelly ◽  
...  

Abstract Date Presented 04/13/21 Individuals with autism spectrum disorder (ASD) experience high rates of unemployment. To address this concern, a systematic review was conducted to examine the efficacy of work-related training for individuals with ASD. The results suggest that various elements of work-related training programs prove efficacious in increasing employment outcomes in individuals with ASD, but further research is needed to determine if these methods would be useful in OT practice. Primary Author and Speaker: Marie-Christine Potvin Additional Authors and Speakers: Mindy Garfinkel

YMER Digital ◽  
2021 ◽  
Vol 20 (10) ◽  
pp. 143-150
Author(s):  
Nausheen Hussain ◽  
◽  
Madhurini Vallikad ◽  

This article examined studies published in the years January 2016 to December 2020 in order to conduct a systematic review of articles related to parent training programs or interventions conducted on parents of children with autism spectrum disorder (ASD) and its effect on improving child outcomes and parent outcomes in day to day life. A total of 14 articles were selected from 589 abstracts which matched the inclusion criteria. The systematic analysis showed that results of these studies were mostly inconclusive as more work needed to be done in the form of using structured parent training programs or interventions. Though the need for parent training programs or interventions have been recognized more research needs to be done in this field. Recommendations from the study are that more importance should be given to improving parental outcomes in order to improve child outcomes and this should be the focus of all pediatric as well as other professionals working with children on the spectrum


2020 ◽  
Vol 29 (2) ◽  
pp. 890-902
Author(s):  
Lynn Kern Koegel ◽  
Katherine M. Bryan ◽  
Pumpki Lei Su ◽  
Mohini Vaidya ◽  
Stephen Camarata

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.


2018 ◽  
Vol 19 (5) ◽  
pp. 454-459 ◽  
Author(s):  
Francielly Mourao Gasparotto ◽  
Francislaine Aparecida dos Reis Lívero ◽  
Sara Emilia Lima Tolouei Menegati ◽  
Arquimedes Gasparotto Junior

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.


Author(s):  
Huaimin Yi ◽  
Yajun Han ◽  
Mengxin Li ◽  
Jiong Wang ◽  
Liping Yang

2021 ◽  
pp. 116856
Author(s):  
Frédéric Dutheil ◽  
Aurélie Comptour ◽  
Roxane Morlon ◽  
Martial Mermillod ◽  
Bruno Pereira ◽  
...  

Author(s):  
Mizuho Takayanagi ◽  
Yoko Kawasaki ◽  
Mieko Shinomiya ◽  
Hoshino Hiroshi ◽  
Satoshi Okada ◽  
...  

AbstractThis study was a systematic review of research using the Wechsler Intelligence Scale for Children (WISC) with Autism Spectrum Disorder (ASD) to examine cognitive characteristics of children with ASD beyond the impact of revisions based on WISC and diagnostic criteria changes. The classic “islets of ability” was found in individuals with full-scale IQs < 100. The “right-descending profiles” were observed among high IQ score individuals. High levels on the Block Design and low Coding levels were consistently found regardless of the variation in intellectual functioning or diagnosis. This review identified patterns of cognitive characteristics in ASD individuals using empirical data that researchers may have previously been aware of, based on their experiences, owing to the increased prevalence of ASD.


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