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Managing Disruptions in Early Care & Education: Lessons from COVID-19.

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Title: Managing Disruptions in Early Care & Education: Lessons from COVID-19.
Authors: Cook, Kyle DeMeo1 (AUTHOR) kdcook@bu.edu, Ferreira van Leer, Kevin2 (AUTHOR), Gandhi, Jill3 (AUTHOR), Ayala, Carolina2 (AUTHOR), Kuh, Lisa P.4 (AUTHOR)
Source: Early Childhood Education Journal. Feb2024, Vol. 52 Issue 2, p281-292. 12p.
Subject Terms: *Kindergarten children, *Child labor, *COVID-19 pandemic, *Families, COVID-19, Decision making in children, Spring
Abstract: Families face challenging decisions about early care and education (ECE) for their children. Decisions about what is best for each child and family are constrained by family and contextual factors and are prone to disruptions. This study provides a descriptive look at patterns of ECE settings children were in the year prior to kindergarten, beginning in Fall 2019 through Spring 2021, a period during which most ECE arrangements were disrupted by the COVID-19 pandemic, and into the 2020–2021 kindergarten year. Analyses of survey (N = 121) and interview (n = 25) data from families whose children entered kindergarten in Fall 2020 revealed multiple and cascading disruptions during this time. Disruptions were nearly universal, and families made continual accommodations as they juggled employment needs and children's ECE needs. Findings from this study have implications for how actual and anticipated disruptions may have a greater influence on families' child care decision-making into the future. [ABSTRACT FROM AUTHOR]
Copyright of Early Childhood Education Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: *<searchLink fieldCode="DE" term="%22Kindergarten+children%22">Kindergarten children</searchLink><br />*<searchLink fieldCode="DE" term="%22Child+labor%22">Child labor</searchLink><br />*<searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink><br />*<searchLink fieldCode="DE" term="%22Families%22">Families</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making+in+children%22">Decision making in children</searchLink><br /><searchLink fieldCode="DE" term="%22Spring%22">Spring</searchLink>
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  Data: Families face challenging decisions about early care and education (ECE) for their children. Decisions about what is best for each child and family are constrained by family and contextual factors and are prone to disruptions. This study provides a descriptive look at patterns of ECE settings children were in the year prior to kindergarten, beginning in Fall 2019 through Spring 2021, a period during which most ECE arrangements were disrupted by the COVID-19 pandemic, and into the 2020–2021 kindergarten year. Analyses of survey (N = 121) and interview (n = 25) data from families whose children entered kindergarten in Fall 2020 revealed multiple and cascading disruptions during this time. Disruptions were nearly universal, and families made continual accommodations as they juggled employment needs and children's ECE needs. Findings from this study have implications for how actual and anticipated disruptions may have a greater influence on families' child care decision-making into the future. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Early Childhood Education Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10643-022-01430-2
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              Text: Feb2024
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