Children And Families
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PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258959
Franceli L. Cibrian ◽  
Elissa Monteiro ◽  
Elizabeth Ankrah ◽  
Jesus A. Beltran ◽  
Arya Tavakoulnia ◽  

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children’s learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google calendars were synchronized across devices to guide children through daily schedules. After the sixth week, we conducted parent interviews to understand the use of smartwatches and the impact on children’s functioning, and we collected physiological data directly from the smartwatch. Our results demonstrated that children successfully adopted the use of the smartwatch, and parents believed the intervention was helpful, especially in supporting the development of organizational skills in their children. Overall, we illustrate how even simple DHIs, such as using smartwatches to promote daily organization and task completion, have the potential to support children and families, particularly during periods of distance learning. We include practical suggestions to help professionals teach children with ADHD to use smartwatches to improve organization and task completion, especially as it applies to supporting remote instruction.

2021 ◽  
pp. 104973152110500
Richard P. Barth ◽  
Jill Duerr Berrick ◽  
Antonio R. Garcia ◽  
Brett Drake ◽  
Melissa Jonson-Reid ◽  

An intense appetite for reforming and transforming child welfare services in the United States is yielding many new initiatives. Vulnerable children and families who become involved with child welfare clearly deserve higher quality and more effective services. New policies, programs, and practices should be built on sound evidence. Reforms based on misunderstandings about what the current data show may ultimately harm families. This review highlights 10 commonly held misconceptions which we assert are inconsistent with the best available contemporary evidence. Implications for better alignment of evidence and reform are discussed.

Genealogy ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 89
John Wainwwright

This Special Issue explores papers on the experiences of children, young people and families of Black, Asian and Minority Ethnic (BAME) heritage who come into contact with the criminal (youth) justice systems in the UK [...]

Myrthe van den Broek ◽  
Puvaneswary Ponniah ◽  
P. Judy Ramesh Jeyakumar ◽  
Gabriela V. Koppenol-Gonzalez ◽  
John Vijay Sagar Kommu ◽  

Abstract Background Most children and adolescents in need of mental healthcare remain untreated even when services are available. This study evaluates the accuracy of a new tool, the Community Case Detection Tool (CCDT). The CCDT uses illustrated vignettes, two questions and a simple decision algorithm to support proactive community-level detection of children, adolescents and families in need of mental healthcare to improve help-seeking. Methods Trusted and respected community members in the Eastern Province of Sri Lanka used the CCDT in their daily routine. Children and families detected as potentially in need of mental healthcare based on utilizing the CCDT (N = 157, aged 6–18 years) were invited for a clinical interview by a mental health counsellor using the Mini-International Neuropsychiatric Interview for Children and Adolescents (MINI-KID). The CCDT results were compared against the results of the clinical interview. The concurrent validity and performance of the CCDT were also evaluated by comparing the CCDT outcomes against the Strengths and Difficulties Questionnaire (SDQ). Results 7 out of 10 children and families detected by community members using the CCDT were confirmed to be in need for treatment (positive predictive value [PPV] = 0.69; 0.75 when compared to the SDQ). Detections based on the family problem vignette were most accurate (PPV = 0.76), followed by the internalising problem vignette (PPV = 0.71) and the externalising problem vignette (PPV = 0.62). Conclusions The CCDT is a promising low-cost solution to overcome under-detection of children and families in need of mental healthcare. Future research should focus on evaluating the effectiveness, as well as additional strategies to improve help-seeking.

Heather L. Rouse ◽  
Rebecca J. Bulotsky Shearer ◽  
Sydney S. Idzikowski ◽  
Amy Hawn Nelson ◽  
Mark Needle ◽  

The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.

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