Dataset Information

Mainstream Preschool Teachers' Skills at Identifying and Referring Children with Autism Spectrum Disorder (ASD).

ABSTRACT: Background: Early intervention will help children with autism spectrum disorder (ASD) to attain early learning reinforcement. This study focuses on exploring the ability of preschool teachers to identify children with ASD and their referral decision-making process. Method: This is a mixed-method study (qualitative and quantitative methods) involving 20 respondents. The qualitative study is based on an open question case study, while the quantitative study consists of questionnaire with demographic variables to identify the effect of the demographic variables on the preschool teachers' ability to identify children with ASD. Sample: The sample was selected via convenience sampling among mainstream preschool teachers. The data was analyzed using SPSS software and thematic analysis. Results: The findings show that preschool teachers did not have skills at identifying children with ASD, and the majority of them labelled children with ASD as spoilt or hyperactive children. They also viewed children with ASD as having other disorders such as attention deficit hyperactivity disorder (ADHD) or communication disorders such as introversion. Moreover, preschool teachers expressed that the reason for the child's behaviour could be due to the parents' inability to properly educate their child. Additionally, the demographic variables of the preschool teachers, such as age, education level and teaching experience, were found not to affect their ASD identification skills. Conclusion: Preschool teachers need to improve their skills in identifying ASD among children via training.

SUBMITTER: Mohammed Taresh S

PROVIDER: S-EPMC7344551 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Mainstream Preschool Teachers' Skills at Identifying and Referring Children with Autism Spectrum Disorder (ASD).

Mohammed Taresh Sahar S Aniza Ahmad Nor N Roslan Samsilah S Ma'rof Aini Marina AM Mohammed Zaid Sumaia S

International journal of environmental research and public health 20200616 12

<i>Background:</i> Early intervention will help children with autism spectrum disorder (ASD) to attain early learning reinforcement. This study focuses on exploring the ability of preschool teachers to identify children with ASD and their referral decision-making process. <i>Method:</i> This is a mixed-method study (qualitative and quantitative methods) involving 20 respondents. The qualitative study is based on an open question case study, while the quantitative study consists of questionnaire ...[more]

PMID: 32560042

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Project description:The aims of this study are to (1) conduct a systematic review of the intervention literature in preschool children with autism spectrum disorder (ASD), including types of interventions that are tested and the classification of outcome measures used and (2) to undertake a meta-analysis of the studies, allowing for the first time the comparison of different approaches to intervention using comparative outcomes. There are a number of alternative modalities of intervention for preschool children with ASD in use with different theoretical background and orientation, each of which tend to use different trial designs and outcome measures. There is at this time an urgent need for comprehensive systematic review and meta-analyses of intervention studies for preschool children with ASD, covering studies of adequate quality across different intervention types and measurement methods, with a view to identifying the best current evidence for preschool interventions in the disorder.The authors will perform a systematic review of randomised controlled trials for preschool children with ASD aged 0-6 years, along with a meta-analysis of qualifying studies across intervention modality. The authors will classify the interventions for preschool children with ASD under three models: behaviour, multimodal developmental and communication focused. First, the authors will perform a systematic review. Then, the authors will conduct a meta-analysis by comparing the three models with various outcomes using an inverse variance method in a random effect model. The authors will synthesise each outcome of the studies for the three models using standardised mean differences.This study will identify each intervention's strengths and weaknesses. This study may also suggest what kinds of elements future intervention programmes for children with ASD should have. The authors strongly believe those findings will be able to translated into the clinical practices and patients and their family benefits. Review registration: PROSPERO CRD42011001349.

Project description:Background: Autism spectrum disorder (ASD) is a developmental disorder characterized by deficits in social interaction, restricted interest and repetitive behavior. Oxidative stress in response to environmental exposure plays a role in virtually every human disease and represents a significant avenue of research into the etiology of ASD. The aim of this study was to explore the diagnostic utility of four urinary biomarkers of oxidative stress. Methods: One hundred and thirty-nine (139) children and adolescents with ASD (89% male, average age = 10.0 years, age range = 2.1 to 18.1 years) and 47 healthy children and adolescents (49% male, average age 9.2, age range = 2.5 to 20.8 years) were recruited for this study. Their urinary 8-OH-dG, 8-isoprostane, dityrosine and hexanoil-lisine were determined by using the ELISA method. Urinary creatinine was determined with the kinetic Jaffee reaction and was used to normalize all biochemical measurements. Non-parametric tests and support vector machines (SVM) with three different kernel functions (linear, radial, polynomial) were used to explore and optimize the multivariate prediction of an ASD diagnosis based on the collected biochemical measurements. The SVM models were first trained using data from a random subset of children and adolescents from the ASD group (n = 70, 90% male, average age = 9.7 years, age range = 2.1 to 17.8 years) and the control group (n = 24, 45.8% male, average age = 9.4 years, age range = 2.5 to 20.8 years) using bootstrapping, with additional synthetic minority over-sampling (SMOTE), which was utilized because of unbalanced data. The computed SVM models were then validated using the remaining data from children and adolescents from the ASD (n = 69, 88% male, average age = 10.2 years, age range = 4.3 to 18.1 years) and the control group (n = 23, 52.2% male, average age = 8.9 years, age range = 2.6 to 16.7 years). Results: Using a non-parametric test, we found a trend showing that the urinary 8-OH-dG concentration was lower in children with ASD compared to the control group (unadjusted p = 0.085). When all four biochemical measurements were combined using SVMs with a radial kernel function, we could predict an ASD diagnosis with a balanced accuracy of 73.4%, thereby accounting for an estimated 20.8% of variance (p < 0.001). The predictive accuracy expressed as the area under the curve (AUC) was solid (95% CI = 0.691-0.908). Using the validation data, we achieved significantly lower rates of classification accuracy as expressed by the balanced accuracy (60.1%), the AUC (95% CI = 0.502-0.781) and the percentage of explained variance (R2 = 3.8%). Although the radial SVMs showed less predictive power using the validation data, they do, together with ratings of standardized SVM variable importance, provide some indication that urinary levels of 8-OH-dG and 8-isoprostane are predictive of an ASD diagnosis. Conclusions: Our results indicate that the examined urinary biomarkers in combination may differentiate children with ASD from healthy peers to a significant extent. However, the etiological importance of these findings is difficult to assesses, due to the high-dimensional nature of SVMs and a radial kernel function. Nonetheless, our results show that machine learning methods may provide significant insight into ASD and other disorders that could be related to oxidative stress.

Dataset Information

Mainstream Preschool Teachers' Skills at Identifying and Referring Children with Autism Spectrum Disorder (ASD).

Publications

Mainstream Preschool Teachers' Skills at Identifying and Referring Children with Autism Spectrum Disorder (ASD).

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