scholarly journals Child and Adolescent Anxiety as a Result of the COVID-19 Pandemic

2021 ◽  
Author(s):  
Jie Luo ◽  
Alfred Shaw
Keyword(s):  
Mental Health ◽  
Global Health ◽  
Children And Adolescents ◽  
Clinical Characteristics ◽  
Psychological Impact ◽  
Child And Adolescent ◽  
Therapeutic Options ◽  
Physical And Mental Health ◽  
Adolescent Anxiety ◽  
Global Health Issue

As the coronavirus disease 2019 (COVID-19) pandemic has spread, so has the psychological impact of the disease been felt worldwide. Among the various types of psychological problems that are caused by COVID-19, anxiety poses a great threat to the physical and mental health of children and adolescents. With an aim of advancing the current work of diagnosing and treating child and adolescent anxiety as a result of the COVID-19 pandemic, this chapter discusses this noticeable global health issue focusing on the following key parts: possible etiology, clinical characteristics, diagnosis and available therapeutic options.

scholarly journals Effects of COVID-19 on the economy and mental health of young people in South Africa: opportunities for strengthening social protection programmes by integrating mental health

2021 ◽  
Vol 51 (2) ◽  
pp. 199-204
Author(s):  
Alejandra Álvarez-Iglesias ◽  
Emily Garman ◽  
Crick Lund
Keyword(s):  
Mental Health ◽  
South Africa ◽  
Young People ◽  
Child Support ◽  
Social Protection ◽  
Psychological Impact ◽  
Sub Saharan Africa ◽  
Physical And Mental Health ◽  
Protection Measures ◽  
Employment Education

The majority of COVID-19 cases in sub-Saharan Africa are found in South Africa, where one third of young people are not in employment, education or training. As the world continues to fight the COVID-19 virus spread, an increasing volume of studies are analysing and trying to predict the consequences of the pandemic on the economy and on physical and mental health. This article describes the economic and psychological impact of COVID-19 in South Africa’s youth specifically, the efforts made to tackle these issues, and the opportunities to integrate mental health into the country’s social protection measures, such as the Child Support Grant.

Approaches to Delivering Child and Adolescent Mental Health Services: The South Australian Experience

1995 ◽  
Vol 29 (2) ◽  
pp. 230-237 ◽  
Author(s):  
Michael Gifford Sawyer ◽  
Robert John Kosky
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
South Australia ◽  
Adolescent Mental Health ◽  
Child And Adolescent ◽  
The South ◽  
Number Of Children

Approximately 10% of children and adolescents experience mental health problems, however only a small proportion receive specialised help. Identifying approaches which can provide a balanced and effective service for the large number of children and adolescents with problems is currently a major challenge for child and adolescent mental health services in Australia. In South Australia, following a review in 1983, child and adolescent services were reorganised into two separate but closely related services. This paper draws on experience in South Australia over the last decade to identify approaches which can be employed in six key areas that significantly influence the effectiveness of child and adolescent mental health services. The paper also describes the specific features which were included in the South Australian child and adolescent mental health service to address these issues.

scholarly journals The Choice and Partnership Approach to community mental health and addictions services: a realist-informed scoping review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033247
Author(s):  
Leslie Anne Campbell ◽  
Sharon E Clark ◽  
Caitlyn Ayn ◽  
Jill Chorney ◽  
Debbie Emberly ◽  
...  
Keyword(s):  
Mental Health ◽  
Community Mental Health ◽  
Children And Adolescents ◽  
Scoping Review ◽  
Research Policy ◽  
Research Question ◽  
Grey Literature ◽  
Child And Adolescent ◽  
Online Databases ◽  
Partnership Approach

IntroductionEarly identification and appropriate treatment of child and adolescent mental health disorders can often be hampered by patchwork services with poorly planned or unclear pathways. The Choice and Partnership Approach (CAPA) is an evidence-based transformational model of community (community-based or outpatient) mental health and addictions services for children and adolescents that aims to better match services to needs and to improve timely access to care. CAPA has been variably implemented across jurisdictions but has not been comprehensively evaluated for its impact on system and client outcomes. Our research question is, ‘To what degree does CAPA work, for whom and under what circumstances?’. The purpose of this review is twofold: (1) to gain an understanding of the extent and outcomes of the implementation of CAPA in community mental health and addictions services; and (2) to identify the role of context as it influences the implementation of CAPA and resulting client and system outcomes.Methods and analysisWe will conduct a realist-informed scoping review of the literature related to CAPA in either child and adolescent or adult community mental health and addictions services. Relevant studies, reports and documentation will be identified by searching the following online databases: MEDLINE, Embase, CINAHL, PsycINFO, Academic Search Premier, ERIC, Web of Science, Cochrane, Dissertations Abstracts, NCBI Bookshelf, PubMed Central and the Canadian Health Research Collection. The search strategy was developed by a health sciences library scientist and informed by a multidisciplinary team comprising methodological and content knowledge experts. The search will gather evidence from multiple online databases of peer-reviewed literature and grey literature repositories. All articles will be independently assessed for inclusion by pairs of reviewers. The key themes derived from a thematic analysis of extracted data will be presented in a narrative overview.Ethics and disseminationResearch ethics review is not required for this scoping review. The results will be disseminated through meetings with stakeholders (including clients and families, clinicians and decision-makers), conference presentations and peer-reviewed publication. The results of this review will inform an overarching programme of research, policy and quality indicator development to ultimately improve mental health and addictions care and subsequent mental health outcomes for children and adolescents.

scholarly journals Detection of child depression using machine learning methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261131
Author(s):  
Umme Marzia Haque ◽  
Enamul Kabir ◽  
Rasheda Khanam
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Mental Illness ◽  
Children And Adolescents ◽  
Mental Health Problems ◽  
Confusion Matrix ◽  
Model Performance ◽  
Child And Adolescent ◽  
Target Variable ◽  
Depression Detection

Background Mental health problems, such as depression in children have far-reaching negative effects on child, family and society as whole. It is necessary to identify the reasons that contribute to this mental illness. Detecting the appropriate signs to anticipate mental illness as depression in children and adolescents is vital in making an early and accurate diagnosis to avoid severe consequences in the future. There has been no research employing machine learning (ML) approaches for depression detection among children and adolescents aged 4–17 years in a precisely constructed high prediction dataset, such as Young Minds Matter (YMM). As a result, our objective is to 1) create a model that can predict depression in children and adolescents aged 4–17 years old, 2) evaluate the results of ML algorithms to determine which one outperforms the others and 3) associate with the related issues of family activities and socioeconomic difficulties that contribute to depression. Methods The YMM, the second Australian Child and Adolescent Survey of Mental Health and Wellbeing 2013–14 has been used as data source in this research. The variables of yes/no value of low correlation with the target variable (depression status) have been eliminated. The Boruta algorithm has been utilized in association with a Random Forest (RF) classifier to extract the most important features for depression detection among the high correlated variables with target variable. The Tree-based Pipeline Optimization Tool (TPOTclassifier) has been used to choose suitable supervised learning models. In the depression detection step, RF, XGBoost (XGB), Decision Tree (DT), and Gaussian Naive Bayes (GaussianNB) have been used. Results Unhappy, nothing fun, irritable mood, diminished interest, weight loss/gain, insomnia or hypersomnia, psychomotor agitation or retardation, fatigue, thinking or concentration problems or indecisiveness, suicide attempt or plan, presence of any of these five symptoms have been identified as 11 important features to detect depression among children and adolescents. Although model performance varied somewhat, RF outperformed all other algorithms in predicting depressed classes by 99% with 95% accuracy rate and 99% precision rate in 315 milliseconds (ms). Conclusion This RF-based prediction model is more accurate and informative in predicting child and adolescent depression that outperforms in all four confusion matrix performance measures as well as execution duration.

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