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. Author manuscript; available in PMC: 2025 Oct 31.
Published before final editing as: Early Child Dev Care. 2025 Oct 21:10.1080/03004430.2025.2574979. doi: 10.1080/03004430.2025.2574979

COVID-19 School Disruptions in Early Childhood Education and Children’s Early Elementary School Outcomes: Findings from the Smart Beginnings Randomized Clinical Trial

Elizabeth B Miller 1, Caitlin F Canfield 2, Ashleigh I Aviles 3, Leah J Hunter 4, Daniel S Shaw 5, Alan L Mendelsohn 6, Pamela A Morris-Perez 7
PMCID: PMC12574550  NIHMSID: NIHMS2118890  PMID: 41179929

Abstract

The COVID-19 pandemic has significantly impacted families with young children (age 0–5). Using a subset of data from the randomized clinical trial of an integrated, preventive parenting model, Smart Beginnings (SB), this study examined associations between COVID-19-related school disruptions in early childhood education (ECE) and children’s early elementary school outcomes. A secondary, exploratory aim sought to determine whether SB attenuated these relations. Path analyses demonstrated that school disruptions in ECE were associated with lower literacy skills at age 6 in letter-word identification (β=−.32, p<.01) and phonemic decoding (β=−.26, p<.05), but not for math or oral language skills. School disruptions in ECE were also related to increased internalizing behavior in children (β=.34, p<.01), with a trend for increased externalizing behavior (β=.22, p<.10). There was no significant moderation by SB intervention group. Implications for future school disruptions and acute stressors more broadly, as well as the role of preventive interventions, are discussed.

Keywords: COVID-19, school disruptions, early childhood education, elementary

The COVID-19 pandemic has had significant impacts for families with young children (age 0–5; Courtney et al., 2020; Fahle et al., 2024; U.S. Department of Education [DOE], 2025). Young children have largely been spared acute severe disease impacts of COVID-19 (Lee et al., 2020), but are at considerable risk for delayed socioemotional and academic outcomes because of compromises to their initial learning experiences. Pandemic-related disruptions in early childhood education (ECE; i.e., childcare and preschool) are significant because these settings are important for school readiness, and disruptions occurred across several key parameters of schooling including enrollment, attendance, quality, and stability of the learning environment (Weiland et al., 2021). In addition, because these disruptions may affect underlying family mechanisms more broadly, including increased parental distress and changes in parent-child interactions, school disruptions can have indirect detrimental impacts on children’s outcomes (McLaughlin et al., 2007; Prime et al., 2020).

Such extensive effects are consistent with developmental cascades theory, which suggests that early environments and experiences may lead to impacts across multiple child domains that expand across the lifespan (Masten & Cicchetti, 2010). Thus, in a developmental cascades model, ECE school disruptions may have accumulating adverse effects into middle childhood, adolescence, and even adulthood, emphasizing the multifaceted nature of disasters and the complexities in responding to these challenges effectively.

Moreover, the initial adverse outcomes of ECE school disruptions may be particularly impactful for racial/ethnic minority children and those with low incomes who are at increased risk for disparities in school readiness (Hahn et al., 2014) and have been disproportionately affected by the impacts of COVID-19, including school disruptions (Parolin & Lee, 2021). Thus, research on the impacts of COVID-19 disruptions in ECE is critical to understanding the sequelae of pandemic-related school disruptions on children’s development across the lifespan and especially for racial/ethnic minority children and those with low incomes.

The current study seeks to examine the association between parent-reported COVID-19-related school disruptions in ECE and children’s early elementary school outcomes. Based on the current sample’s randomized clinical design, as a secondary, exploratory aim we also sought to determine whether an integrated, tiered preventive parenting model, Smart Beginnings (SB), attenuates these relations. The findings have the potential to advance our understanding of the pandemic’s impacts at a critically sensitive period in development.

The Role of ECE in Mitigating Disparities Resulting from Poverty

Poverty-related disparities in early development and school readiness among young children have led to early gaps in reading and math (Reardon & Portilla, 2016), in conjunction with increased externalizing and inattentive behaviors (Comeau & Boyle, 2017). High quality ECE has been found to reduce disparities in children’s development, as it is associated with gains in educational attainment (Fram et al., 2012), achievement (Duncan et al., 2011), and economic well-being across the lifespan (Duncan et al., 2010). The benefits of ECE are particularly salient for racial/ethnic minority populations and those with low incomes (Hahn et al., 2014).

Prior Research on Impacts of School Disruptions in ECE

Despite the benefits of ECE, however, disruptions in young children’s educational environments have historically been understudied in the context of disasters such as severe weather incidents (Chemtob et al., 2010), and few prior crises have had the universal reach of the COVID-19 pandemic. Nevertheless, research examining the impact of disasters on school-age children provides an indication and suggests long-term delays in academic and socioemotional skills. For example, after Hurricane Katrina, it took two years for students to make up the learning lost while schools were closed (Harris & Larsen, 2019). Further, in the Australian bushfires, 1st grade students in impacted schools showed lower gains in reading and math through 5th grade (Gibbs et al, 2019). Disruptions in ECE may be especially critical, as preschool students are just becoming part of their school communities and building the foundations for school success (Stormont et al., 2019).

The few studies conducted with young children following disasters support this. For instance, after Hurricane Hugo, 67% of parents of preschoolers reported increases in externalizing behavior (Swenson et al., 1996). Disruptions in ECE are also likely to exacerbate pre-existing family vulnerabilities (e.g., maternal depression), leading to widening, cascading disparities in children’s development (Masten & Cicchetti, 2010). The current pandemic presents a unique opportunity to examine the impacts of disruptions for young children because of its nearly universal reach, together with variation in school disruptions and child impacts (Van Lancker & Parolin, 2020).

Covid-19-Related School Disruptions in ECE

Nearly all U.S. children in educational settings of any type (~98%), including those in childcare and preschool, experienced school disruptions in the initial period of COVID-19 (March-June 2020; Education Week, 2021). However, in Fall 2020, school disruptions varied, with greater disruption in racial/ethnic minority communities (Parolin & Lee, 2021). For this study, we conceptualized school disruptions as the parent-reported variation in schooling (childcare/preschool) that young children experienced attributable to the pandemic. Such disruptions occurred across key parameters of ECE, including enrollment, attendance, quality, and stability of the learning environment.

COVID-19-Related Disruptions in ECE Enrollment and Attendance

During the pandemic, enrollment and attendance were disrupted by remote-only and hybrid learning models implemented in districts, as well as by variation in parents’ ability to oversee children’s remote learning. As ECE (and even kindergarten) are not compulsory in most states (Kelley et al., 2020), starting in Fall 2020, there were particularly steep drops in preschool enrollment. National preschool enrollments saw a net decline of ~25% (~250,000 students; Friedman-Krauss et al., 2022), with even steeper drops among children from families with low incomes (~14%; Dee et al., 2021).

Even among those enrolled in ECE, attendance dropped during the 2020–2021 school year (Weiland et al., 2021). In subsequent years, chronic absenteeism continued. For example, in New York City, the largest school district in the U.S., 36% of enrolled students were chronically absent in 2022–2023 (Steinberg, 2023). Attendance in remote schooling was also impacted by teachers’ and parents’ challenges managing new technology, internet issues, and difficulties in supporting preschool children in remote learning (Ford et al., 2021).

COVID-19-Related Disruptions in Quality and Stability of the ECE Learning Environment

The quality of young children’s learning experiences declined during the pandemic (Weiland et al., 2021). The increased focus on health practices in schools reduced instructional time. Social distancing requirements also resulted in fewer learning opportunities around sharing and conflict resolution. Staff shortages and periodic closures disrupted normal routines for children (Friedman-Krauss et al., 2022). Similarly, quality of remote instruction suffered as many teachers reported poor fit with young children’s developmental needs (Ford et al., 2021; Lynch et al., 2023). The pandemic also negatively affected stability, as remote-only and hybrid models limited the hours children spent in formal learning environments, and often included last minute changes (Ford et al., 2021).

Impacts of COVID-19-Related School Disruptions on Children’s Outcomes

Aggregated data from 41 states suggest that nearly 50% of U.S. kindergarteners were below grade-level benchmarks in the 2020–21 school year (the first year following pandemic-onset; Amplify, 2021). Though recent national data on elementary school children indicate that students in several states have recovered some pandemic-related learning losses (Fahle et al., 2024), the gains did not fully ameliorate these losses and especially for racial/ethnic minority students with low incomes (U.S. DOE, 2025).

Additional emerging evidence suggests that preschool children learned less during the pandemic, especially in early language skills (Nevo, 2023) and for those who had fewer school days in person (Lynch et al., 2023). Teachers and parents also reported elevated rates of socioemotional problems (Jung & Barnett, 2021). Given the potential for early experiences to have long-term impacts across domains (Masten & Cicchetti, 2010), additional research is needed to examine the effects of ECE school disruptions as children enter elementary school and reading and math curricula accelerate.

The Smart Beginnings (SB) Model

Preventive parenting interventions have the potential to mitigate the adverse effects of stressors, including those related to disasters. SB is an innovative, tiered parenting model designed to address poverty-related disparities in child development (Shaw et al., 2021). It consists of two tiers – a universal healthcare-based component (PlayReadVIP; Mendelsohn et al., 2005; PlayReadVIP, n.d.) and a targeted home visiting component (the Family Check-Up [FCU]; Dishion & Stormshak 2007). PlayReadVIP is provided universally in pediatric primary care from birth to age three years at the time of well-child visits to promote child development. SB’s targeted prevention program, the FCU, is provided in the home for a subset of families with additional risk factors to improve the caregiving environment and child behavior. Readers are referred to Shaw et al. (2021) for a thorough description of the SB model and the individual intervention components of PlayReadVIP and the FCU. Prior research in a randomized clinical trial (RCT) of the SB model has documented its efficacy in improving parental support of cognitive stimulation from infancy and toddlerhood (Miller et al, 2023) and in indirectly improving child outcomes at age 4 through these changes in parental cognitive stimulation (Miller et al., 2024a).

As the goal of SB is to increase supportive parenting, reduce parenting stress, and foster parent-child relationships, families who participated in the SB model may have additional assets that prepared them for COVID-19 school disruptions and their children spending more time at home. Theoretically, such improved family functioning and child skills could then generate a positive developmental cascade across later domains of child development through adolescence (Bornstein et al., 2013).

Present Study

The primary aim of the present study was to examine associations between parent-reported COVID-19 school disruptions in ECE and children’s academic and socioemotional skills at age 6. Based on the growing evidence indicating negative effects of the pandemic for young children (Courtney et al., 2020; Fahle et al., 2024; U.S. DOE, 2025), we hypothesized that increased disruptions in children’s ECE experiences would be negatively associated with children’s academic and socioemotional outcomes. Based on the current sample’s randomized clinical design, a secondary, exploratory study aim also examined whether random assignment to the SB model attenuated these relations. We hypothesized that SB would attenuate the negative associations between ECE disruptions and children’s academic and socioemotional outcomes (Miller et al., 2024b).

Method

This study was part of the single-blind, two-site RCT of SB. As mentioned above, SB is an integrated model to address poverty-related disparities in school readiness through two tiers – a universal healthcare-based program (ReadPlayVIP) and a targeted prevention program through home visiting (the FCU). The original SB RCT consisted of 403 Medicaid-eligible families in NYC (n=200) and in Pittsburgh, PA (n=203). Mothers and infants were recruited for the RCT of SB in postpartum hospital units using a two-phased enrollment process between June 2015 and October 2017. In phase one, families were recruited and in phase two they were randomized at each site to the SB or control conditions by child age 6 weeks. Readers are referred to Miller et al. (2023) and Miller et al. (2024a) for a complete list of the SB RCT’s inclusion criteria.

All families randomly assigned to the treatment condition were offered the PlayReadVIP component of SB (treatment arm, n = 201). A subset of SB treatment families with high levels of psychosocial risk factors were additionally offered the FCU. Screening for FCU eligibility occurred at child age 6 and 18 months and included one primary criterion (e.g., maternal depression, family violence) or at least two secondary criteria (e.g., child behavior challenges, maternal stress). Around 50% of SB treatment families met eligibility criteria. Families randomly assigned to the control condition (control arm, n=202) received routine pediatric care, including anticipatory guidance and developmental surveillance. Informed consent was obtained from all study participants, IRB approval was obtained (FY2016-408; S14-01764; STUDY19040158), and the study is registered in clinicaltrials.gov (NCT02459327).

When the pandemic began in March 2020, delivery of the SB model had been completed, so it was not otherwise affected by the COVID-19 pandemic. As such, this study is uniquely situated to examine how this pre-pandemic family support affected children during subsequent pandemic disruptions.

Participants

The full SB sample was composed of 403 mothers with low incomes, with about a third primiparous. The majority of mothers in NYC were Latine (84%), whereas in Pittsburgh they were predominantly Black/African-American (81%). There were no significant differences on baseline characteristics between treatment and control groups within each site (the unit of randomization; F(9, 350)=1.11, p=.35).

For this particular study, the analytic sample consisted of 112 families whose children were enrolled in center-based ECE during the 2020–2021 school year and had parent-reported data on school disruptions (see Measures, below). Descriptive statistics of the analytic sample are presented in Table 1. Half of the target children were female (48%). The majority of mothers in the sample were Black/African-American (32%) or Latine (58%), with a third primiparous. Most were legally married or cohabitating with a partner (65%) and high school graduates (64%). Given the greater ecology of poverty, families had very low incomes below the poverty threshold and lived in crowded conditions. Lastly, there were no significant differences between treatment and control groups in ECE enrollment (p=.19) or on any baseline characteristic (F(8, 92)=1.28, p=.26).

Table 1.

Descriptive Statistics of Baseline Sociodemographic Characteristics

Mean (SD) / Percent of Sample
Target Child Characteristics
 Gender - female 48%
Maternal Characteristics
 Race/Ethnicity
  Asian 3%
  Black/African-American 32%
  Latine 58%
  White 2%
 Legally married/cohabitating with partner 65%
 Education - high school graduate 64%
 Primiparous birth 33%
 Teenage mother at TC birth 6%
Family Household Characteristics
 Income-to-needs ratioa 0.72 (0.64)
 Crowding ratiob 1.25 (0.61)
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Note. N=112.

a

Income-to-needs ratio of 1.00 indicates that a family is right at the poverty threshold; 2.00 indicates that a family is 200% above that threshold.

b

Crowding ratio indicates how many people live per room in the dwelling. A ratio above one indicates household crowding.

The analytic sample was similar to the full SB sample on the baseline characteristics reported in Table 1, with no significant differences between them (F(8, 352)=1.55, p=.14). We further conducted a Little’s test and found the data in the analytic sample were missing completely at random (MCAR; χ2=68.55, p=.103).

Measures

Assessments were conducted at child ages 4 and 6 years by trained bilingual research assistants masked to randomization group. Surveys were conducted with the target child’s primary caregiver, 99% of which were the mother, in interview format in person or over the phone in their preferred language – English or Spanish.

Primary Predictor – Parent-Reported School Disruptions in ECE

The primary predictor was a composite variable of ECE disruptions to schooling during the COVID-19 pandemic that represented several key parameters of ECE outlined above. These measures were collected from parent survey fielded during the 2020–2021 school year, at the height of the COVID-19 pandemic, when children were approximately age 4.

Disruptions Related to Attendance.

Disruptions related to attendance in ECE consisted of issues related to technology or parent difficulties that limited children’s attendance in remote learning when offered. Technology issues: Parents were asked about technological issues that interfered with remote schooling, including issues with hardware (e.g., not having an appropriate device), software (e.g., programs not working), and internet (e.g., limited data or home Wi-Fi). Scores ranged from 0 to 3, with higher scores representing a greater number of technological issues families experienced (α=0.72). Parent difficulties: Parents were also asked about difficulties related to their own time/ability to help their child during COVID-19 remote learning, including balancing employment and children’s school needs, meeting the needs of multiple children, and not feeling prepared to help with additional schoolwork. This indicator also had a range of 0 to 3, representing the number of difficulties parents perceived (α=0.59).

Disruptions Related to Quality.

Disruptions related to quality in ECE primarily involved the implications of remote learning during the pandemic for children’s development. Parents were asked about concerns they had because of COVID-19-related school disruptions and their child’s development. This included their academic progress, level of social skills, and motivation for learning. Parents received a score of 1 if they endorsed concerns in each of the areas, resulting in a possible score of 0 to 3 (α=0.58).

Disruptions Related to Stability.

Disruptions related to stability in ECE involved disruptions to in-person learning. This included remote or hybrid learning. We examined the amount of time spent in remote learning across the 2020–2021 school year. Families received a score for each semester of the 2020–2021 school year of Fully In Person (0), Hybrid (0.5), or Fully Remote (1), for a total number of remote semesters of 0 to 2 across the year. As detailed above, we consider remote learning in ECE to be a disruption for young children because it limited the hours children spent in formal learning environments.

Primary Outcomes – Child Academic and Socioemotional Skills

The primary outcomes were child academic and socioemotional skills at age 6 years, entirely post-pandemic onset. Three academic domains were assessed – literacy, oral language, and math. Standard scores (M=100, SD=15) were used in all analyses. Spanish-speaking children were administered the same measures as English-speaking children except for one additionally fielded measure detailed below.

Literacy.

Child literacy skills were assessed by the Woodcock-Johnson (WJ) IV (Schrank et al, 2014a; 2014b) Letter-Word Identification test, which measures children’s ability to distinguish letter sounds and words (α=0.98). Spanish-speaking children were additionally assessed by the Woodcock-Muñoz (WM) Identificación de Letras y Palabras test (α=0.97; Muñoz-Sandoval et al., 2005). Spanish-speaking children’s highest scores in either language (English or Spanish) were used in the analyses as an indicator of letter-word identification, as the scores from the WJ and the WM are directly comparable (Pontón & León-Carrión, 2001; Schrank et al., 2014). Literacy skills were also assessed with the Phonemic Decoding Efficiency subtest of the Test of Word Reading Efficiency, Second Edition (TOWRE-2; Torgensen, Wagner, & Rashotte, 2012), which measures phonemic accuracy and fluency (α=.92).

Oral Language.

Child oral language skills were assessed with the WJ IV Oral Comprehension test, which measures children’s ability to understand short oral passages (α=0.83).

Math.

Child math skills were assessed with the WJ IV Applied Problems test, which measures children’s ability to analyze and solve math problems (α=0.92).

Internalizing and Externalizing Behavior.

Child internalizing (α=0.89) and externalizing (α=0.96) behavior were assessed with the Child Behavior Checklist, 6–18 (CBCL; Achenbach & Rescorla, 2000). Parents were presented with a list of child behaviors and reported how true the item was from Not True (0) to Very True or Often True (2). Clinically meaningful T-scores (M=50, SD=10) were used in analyses.

Covariates

Covariates in all models included child gender, maternal education, maternal marital status, family income-to-needs, and SB intervention group. Further, as randomization occurred within each site, site was included in all analyses. Lastly, because data collection was paused early in the COVID-19 pandemic (March-July 2020), we also controlled for child age at the time of assessment to adjust for differences due to this delay (note there were no treatment-control differences in age or timing of assessments).

Analysis Plan

A three-step analytic approach was undertaken in this study. First, a latent variable of school disruption was created from disruption in ECE attendance, quality, and stability. A measurement model was created and individual item loadings were examined to determine appropriateness of the latent variable. Model fit was assessed using several goodness-of-fit statistics: χ2, comparative fit index (CFI), and the root-mean-square error of approximation (RMSEA). A nonsignificant χ2 test, a CFI ≥ .90, and an RMSEA ≤ to .08 indicate good fit. Any individual items that did not load well on the latent variable were examined separately.

Next, to evaluate direct effects between school disruptions in ECE during the COVID-19 pandemic and child academic and socioemotional skills, we conducted path analyses in two separate models using Mplus 8.10 (Muthén & Muthén, 1998–2003). In all models, we controlled for baseline sociodemographic covariates, SB intervention group, site, and age at assessment. Full Information Maximum Likelihood estimation (FIML; Allison, 2003) was used to account for missing data so that all available data were used (n=112; note as mentioned above, missing data were MCAR).

Finally, for the secondary, exploratory aim, we added an interaction term between school disruption in ECE and SB intervention group (SB vs. control) to examine whether SB attenuated relations between ECE school disruptions and child outcomes at age 6.

Using Monte Carlo simulation (Schoemann et al., 2017) with 1,000 replications and 20,000 draws per replication, we estimate that we have >80% power to detect an effect of .30, when we assume small to medium effect sizes from school disruptions in ECE to child outcomes.

Results

Descriptive Statistics

Descriptive statistics of the primary predictor of ECE school disruptions and the child outcomes are presented in Table 2. Results indicated that parents reported an average of 1.71 technological issues related to remote schooling, 1.61 difficulties related to their own time/ability to help with remote schooling, and 1.83 concerns with their child’s development due to the pandemic (possible ranges 0–3). Further, there was significant variation in the amount of in-person schooling children attended during the 2020–2021 school year. About 42% of children attended fully-remote school, whereas about 8% attended exclusively in person (excluding periodic classroom closures due to positive cases). The other 50% attended school in a hybrid model. Figure 1 displays a histogram of this variable.

Table 2.

Descriptive Statistics of Child Outcomes and ECE School Disruption

Mean (SD) / Percent of Sample
Child Outcomes (Age 6)
 Literacy
  Letter-Word Identification 91.41 (22.29)
  Phonemic Decoding 91.13 (16.12)
 Oral Language 83.71 (19.78)
 Math 84.72 (18.46)
 Behavior
  Internalizing 52.29 (10.83)
  Externalizing 55.64 (10.79)
Primary Predictor - School Disruptions in ECE
 Attendance
  Technological Issues 1.71 (1.19)
  Parent Perceived Difficulties 1.61 (1.07)
 Quality
  Child Development Concerns 1.83 (1.11)
 Stability
  Proportion of ECE in Remote Learning
   Fully Remote 42%
   Fully In Person 8%
   Hybrid Model 50%
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Figure 1.

Figure 1.

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Distribution of time spent in ECE remote learning across the 2020–2021 school year.

In outcomes, on average, children in the SB sample scored below national norms (M=100, SD=15), but still within 1 SD, on literacy at age 6 (WJ Letter-Word and TOWRE). However, children scored more than 1 SD below national norms on math (WJ Applied Problems) and oral language (WJ Oral Comprehension). Further, on average, whereas SB children scored close to population norms (M=50, SD=10) on internalizing behaviors, they scored more than ½ SD higher on externalizing behaviors.

Latent Variable of School Disruption

We generated a latent variable of COVID-19-related school disruption from disruptions in ECE attendance (i.e., technology issues and perceived parental difficulties), quality (i.e., child development concerns), and stability (i.e., time in remote learning). While the overall model fit for the model was acceptable (χ2=2.93, p=.23, CFI=.99, RMSEA=.06), time in remote learning loaded poorly (β=.07, p<.55). Therefore, we removed this variable from the model and retained the remaining three variables of technology issues, parent perceived difficulties, and child development concerns. Model fit statistics for this model were unavailable because it was just-identified with only three indicators; however, there were very large and significant factor loadings for all indicators: technology issues (β=.62, p<.001), parent perceived difficulties (β=.80, p<.001), and child development concerns (β=.64, p<.001), suggesting the latent variable of school disruption in ECE using these variables was appropriate.

Relations Between School Disruptions in ECE and Child Early Elementary Outcomes

A full path model of a direct path between the latent variable of school disruptions in ECE and children’s academic and socioemotional skills was tested. Results from the path analyses are displayed in Figures 2 and 3 for academic and socioemotional skills respectively, with estimated standardized parameters for each path.

Figure 2.

Figure 2.

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Standardized direct pathways from school disruption in ECE to child early elementary academic skills.

* p < 0.05. ** p < 0.01. *** p < 0.001.

Figure 3.

Figure 3.

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Standardized direct pathways from school disruption in ECE to child early elementary socioemotional skills.

† p < 0.10. ** p < 0.01. *** p < 0.001.

As shown in Figure 2, a model examining direct paths to children’s academic skills was tested and provided good fit to the data (χ2=24.93, p=.20, CFI=.98, RMSEA=.05). COVID-19 school disruptions in ECE significantly predicted lower literacy skills for both letter-word identification (β=−.33, p<.01) and phonemic decoding (β=−.26, p<.05). The effect sizes were in the medium to large ranges, with explanatory and practical use in both the short and long run (Funder & Ozer, 2019). Such disruptions were not significantly related to math (β=−.08, p=.57) or oral language (β=.03, p=.82) skills.

As shown in Figure 3, a model examining direct paths to children’s internalizing and externalizing behavior was also tested and provided a good fit to the data (χ2=20.81, p=.19, CFI=.98, RMSEA=.05). COVID-19 school disruptions in ECE significantly predicted higher child internalizing behaviors (β=.35, p<.01), with a trend for higher externalizing behaviors (β=.22, p<.10). The effect sizes were again in the medium to large ranges. Post-hoc comparisons (Lenhard & Lenhard, 2014) of the magnitudes of these associations did not reveal them to be significantly different from one another, indicating that COVID-19 school disruptions in ECE likely affected both children’s internalizing and externalizing symptoms, even if the latter did not reach the traditional threshold for significance.

Moderation by SB

For the secondary, exploratory aim on whether SB attenuated relations between school disruptions in ECE and child outcomes at age 6, an interaction term was added to each outcome model between the latent variable of school disruption in ECE and SB intervention group. The interaction term was not significant in either model, indicating that there was no significant moderation of the relation between school disruption in ECE and child outcomes by SB intervention group.

Additional Analyses

Despite the measure of time spent in remote learning not loading well on the latent variable of school disruption in ECE, based on national discourse on the ramifications of remote learning during the pandemic for children’s development, together with emerging evidence that preschoolers who attended more in-person ECE had better learning outcomes than those who attended less (Lynch et al., 2023), we sought to examine whether time spent in ECE remote learning on its own predicted child outcomes at age 6. We therefore conducted additional analyses using Stata 18 (StataCorp, 2021) in which we tested time spent in remote learning during the 2020–2021 school year on child outcomes, controlling for the same covariates above. Results are presented in Table 3 and indicate that the amount of time children learned remotely during ECE was not significantly related to any outcome.

Table 3.

Time in Remote Learning in ECE on Child Elementary Outcomes

Time in Remote Learning
β SE p-value
Child Outcomes (Age 6)
 Literacy
  Letter-Word Identification 0.11 3.43 0.33
  Phonemic Decoding 0.18 2.79 0.12
 Oral Language 0.01 3.06 0.93
 Math 0.14 3.49 0.24
 Behavior
  Internalizing −0.07 1.53 0.46
  Externalizing 0.00 1.50 0.97
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Discussion

This study examined relations between COVID-19 school disruptions in ECE and the academic and socioemotional skills of children at age 6. In line with growing evidence on the negative effects of the pandemic for young children (Courtney et al., 2020; Fahle et al., 2024; U.S. DOE, 2025), we found that increased disruptions in children’s ECE experiences were negatively associated with children’s academic and socioemotional outcomes. Specifically, increased school disruptions were associated with poorer child literacy skills, though not math or oral language. Increased school disruptions were also associated with increased internalizing behaviors, with a trend for externalizing behavior.

Although we had hypothesized that we would find negative relations for all academic outcomes, we found that children’s literacy skills (i.e., letter-word recognition and phonemic decoding) were an area particularly affected by pandemic learning disruptions. During the early elementary school years, reading instruction typically emphasizes word-level reading skills, including letter recognition, letter-sound correspondence, and phonemic awareness, as well as basic, high frequency vocabulary known as sight words (Scarborough, 2001). Such skill building starts as a primary focus of instruction in ECE, and recent research suggests that ECE classrooms spend relatively more time on formal literacy instruction compared with other academic domains, such as math (Mazzocco et al., 2024). Moreover, the math measure used in this study, the WJ Applied Problems test, has many strengths including strong reliability and a large normative data sample. However, researchers have raised some concerns regarding the test’s sensitivity in use with young children and its attention to developmental sequences in math (Clements et al., 2008; Weiland & Yoshikawa, 2013), underscoring its potential limitations.

In addition, the percentage of parents reporting they engaged in literacy activities at home with their child post pandemic, including reading to their child three or more times a week, decreased from pre-pandemic levels (Jung & Barnett, 2021). Accordingly, given the particular focus on literacy skills in ECE settings, combined with decreases in parental home literacy activities post pandemic, disruptions in children’s ECE experiences may be especially salient for literacy outcomes and less so for other academic domains like math, which were less emphasized in the ECE classroom even pre-pandemic and whose measurement proves more challenging. This potential explanation is additionally supported by the fact that as reported above, in the SB RCT, children on average scored close to population norms (M=100, SD=15) on measures of literacy, but more than 1 SD lower on measures of math and oral language.

Similarly, we had hypothesized that ECE school disruptions would be negatively associated with children’s socioemotional skills, including both increased internalizing and externalizing behaviors, consistent with other emerging work in this area (Jung & Barnett, 2021). We found evidence in support of increased internalizing behaviors. The negative association between ECE school disruptions and externalizing behavior only reached marginal significance; however, post hoc comparisons (Lenhard & Lenhard, 2014) indicated that COVID-19 school disruptions in ECE likely affected both sets of symptoms. Prior research demonstrates that large, uncontrollable external events, such as a pandemic, can have negative consequences for the development of internalizing symptoms such as anxiety and depression, as well as externalizing symptoms of aggression and disruptive behavior (Rubens et al., 2018). The results of this study support this contention.

The results of this study on the negative associations of ECE school disruptions and children’s academic and socioemotional skills converge with previous research on the negative impacts of school disruptions in the context of natural disasters such as hurricanes Hugo (Swenson et al., 1996) and Katrina (Harris & Larsen, 2019) and the Australian bush fires (Gibbs et al., 2019). The findings further expand current literature by examining school disruptions in the context of the universal reach of the pandemic (Chemtob et al., 2010; Van Lancker & Parolin, 2020). Emerging evidence on the effects of the pandemic for young children (age 0–5) indicate poorer early language and literacy skills (Nevo, 2023) and increased socioemotional problems (Jung & Barnett, 2021). The results of our study add to these recent findings and indicate that disruptions in children’s ECE experiences have cascading negative effects into elementary school (Masten & Cicchetti, 2010) and may be particularly impactful for children’s literacy skills and behavior.

A secondary, exploratory aim of this study further examined whether the SB model would attenuate the negative associations between school disruptions and child development. We found no evidence of this attenuation, and the lack of moderation by SB suggests that the universal reach of the COVID-19 pandemic constrained the ability of the SB model (completed before the pandemic) to mitigate adverse impacts on child development post pandemic-onset. However, findings should be interpreted with caution given that the analytic sample utilized here represented a specific subset of the original cohort. Notably, we have shown evidence of sustained impacts of the SB intervention with impacts on parenting at age 2 years mediating improvements in child outcomes at age 4 years (Miller et al., 2023; Miller et al., 2024a).

Interestingly, although the detrimental effects of remote learning for young children’s development have dominated national pandemic discourse, and recent work demonstrated that preschoolers who attended more in-person ECE had better outcomes than those who attended less (Lynch et al., 2023), our study did not find that increased time in remote learning predicted child outcomes. Instead, issues around attendance and quality of remote learning, such as technology, limitations on parents’ time/ability to scaffold their children, and pandemic-related developmental concerns about their children, were significant predictors in our study. This implies that children can indeed have resilient developmental trajectories during national crises when the quality of their schooling is assured. Moreover, school leaders can help ensure such quality and help families support their children during future school disruptions and other acute stressors more broadly. This includes ensuring appropriate devices for children, Wi-Fi hotspots, and providing teacher or other staff support for remote learning so parents do not feel too overwhelmed. Further, schools can provide parents with strategies and materials to support home learning, including assistance around child motivation and engagement in learning (Cowden et al., 2020).

Limitations and Future Directions

This study had multiple strengths such as a diverse racial/ethnic minority sample, the ability to examine longitudinal relations between COVID-19 school disruptions in ECE and children’s elementary school outcomes, and testing such associations within the larger context of an RCT. Nonetheless, it also had some limitations. First, our primary predictor of school disruption in ECE was not randomly assigned and therefore our estimates are not based on the experimental design of the original SB RCT. However, we assume the schools attended by study children are relatively homogenous with respect to income (due to racial and socioeconomic residential segregation in NYC and Pittsburgh [Orfield, 1980]) and overarching city-level COVID policies. If we can additionally assume that the variability in school disruption was reasonably idiosyncratic (e.g., due to randomness in who became ill necessitating classroom closures or differences in individual school options that parents had for in-person vs. remote learning) and was independent of children’s outcomes, then we can interpret the estimates causally (Gelman et al., 2021).

Second, although this study did not find that SB attenuated relations between school disruptions and children’s outcomes, future research can examine additional mechanisms that may have been protective during the COVID-19 pandemic, such as more positive parent-child interactions, as other work indicates that such positive interactions increased during the pandemic (Martin et al., 2025). Future work can also continue to examine “sleeper” effects of SB that may emerge as children age as there is strong theoretical support for the mechanism by which SB may attenuate the longer-term impacts of external disruptions (Bornstein et al., 2013). As an example, the New Beginnings parenting intervention did not reveal shorter-term positive effects on child outcomes, but did so five years after the intervention was delivered (see Sandler et al., 2019). Future research can also study later timepoints and include additional predictors such as maternal employment during the pandemic, as well as outcomes such as school progress (e.g., test scores, graduation rates) and teacher reports of children’s academic skills and behavior, to uncover potential longer-term impacts.

In sum, this study found that COVID-19 school disruptions in ECE were negatively associated with some academic and socioemotional outcomes for children in early elementary school. The findings add to the increasing number of studies examining the significant impacts of the pandemic for families with young children and highlight the implications for policies supporting families during future school disruptions and acute stressors more broadly.

Funding:

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD076390. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Clinical trials registration: This study was registered in clinicaltrials.gov (NCT02459327).

Competing Interests: The authors have no relevant financial or non-financial competing interests to report.

Ethics Approval: Informed consent was obtained from all study participants and IRB approval was obtained from New York University (FY2016–408), NYU Grossman School of Medicine (S14–01764), and the University of Pittsburgh (STUDY19040158).

Contributor Information

Elizabeth B. Miller, NYU Grossman School of Medicine

Caitlin F. Canfield, NYU Grossman School of Medicine

Ashleigh I. Aviles, New York University

Leah J. Hunter, University of Pittsburgh

Daniel S. Shaw, University of Pittsburgh

Alan L. Mendelsohn, NYU Grossman School of Medicine

Pamela A. Morris-Perez, New York University

Data sharing statement:

De-identified data will be made available to interested researchers through the establishment of data sharing agreements. Data will be made available to researchers who provide a methodologically sound proposal for use in achieving the goals of the approved proposal. Proposals should be submitted to Pamela Morris-Perez (pamela.morris@nyu.edu).

References

  1. Achenbach TM, & Rescorla LA (2001). Manual for the ASEBA: School-age Forms & Profiles. University of Virginia Research Center for Children, Youth, & Families. [Google Scholar]
  2. Amplify. (2021). COVID-19 means more students not learning to read. Retrieved from go.info.amplify.com/download-whitepaper-fy21_general_moydata_national_readingresearch
  3. Azevedo JP, Hasan A, Goldemberg D, Aroob Iqbal S, & Geven K (2020). Simulating the potential impacts of COVID-19 school closures on schooling and learning outcomes: A set of global estimates. The World Bank Research Observer, 36(1), 1–40. 10.1093/wbro/lkab003 [DOI] [Google Scholar]
  4. Bornstein MH, Hahn CS, & Wolke D (2013). Systems and cascades in cognitive development and academic achievement. Child Development, 84(1), 154–162. 10.1111/j.1467-8624.2012.01849.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chemtob CM, Nomura Y, Rajendran K, Yehuda R, Schwartz D, & Abramovitz R (2010). Impact of maternal posttraumatic stress disorder and depression following exposure to the September 11 attacks on preschool children’s behavior. Child Development, 81(4), 1129–1141. 10.1111/j.1467-8624.2010.01458.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Clements DH, Sarama JH, & Liu XH (2008). Development of a measure of early mathematics achievement using the Rasch model: The Research-based Early Math Assessment. Educational Psychology, 28, 457–482. 10.1080/01443410701777272 [DOI] [Google Scholar]
  7. Comeau J, & Boyle MH (2017). Patterns of poverty exposure and children’s trajectories of externalizing and internalizing behaviors. SSM - Population Health, 4, 86–94. 10.1016/j.ssmph.2017.11.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Conger RD, & Elder GH Jr. (1994). Families in troubled times: Adapting to change in rural America. New York: Aldine de Gruyter. [Google Scholar]
  9. Courtney D, Watson P, Battaglia M, Mulsant BH, & Szatmari P (2020). COVID-19 impacts on child and youth anxiety and depression: Challenges and opportunities. The Canadian Journal of Psychiatry, 65(10), 688–691. 10.1177/0706743720935646 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cowden G, Mitchell P, & Taylor-Guy P (2020). Remote learning rapid literature review. Australian Council for Education Research. [Google Scholar]
  11. Dee TS, Huffaker E, Philips C, & Sagara E (2021). The revealed preferences for school reopening: Evidence from public-school disenrollment. Retrieved from cepa.stanford.edu/content/revealed-preferences-school-reopening-evidence-public-school-disenrollment
  12. Dishion TJ, & Stormshak EA (2007). Intervening in children’s lives: An ecological, family-centered approach to mental health care. Washington: American Psychological Association. 10.1037/11485-000 [DOI] [Google Scholar]
  13. Duncan GJ, Morris PA, & Rodrigues C (2011). Does money really matter? Estimating impacts of family income on young children’s achievement with data from random-assignment experiments. Developmental Psychology, 47(5), 1263–79. 10.1037/a0023875 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Duncan GJ, Ziol-Guest KM, & Kalil A (2010). Early-childhood poverty and adult attainment, behavior, and health. Child Development, 81(1), 306–325. 10.1111/j.1467-8624.2009.01396.x [DOI] [PubMed] [Google Scholar]
  15. Education Week. (2021, June 30). Map: Coronavirus and school closures in 2019–2020. Retrieved from www.edweek.org/leadership/map-coronavirus-and-school-closures-in-2019-2020/2020/03
  16. Fahle E, Kane TJ, Reardon SF, and Staiger DO (2024). The first year of pandemic recovery: A district-level analysis. Center for Education Policy Research. Retrieved from educationrecoveryscorecard.org/wp-content/uploads/2024/01/ERS-Report-Final-1.31.pdf [Google Scholar]
  17. Ford TG, Kwon K-A, & Tsotsoros JD (2021). Early childhood distance learning in the U.S. during the COVID pandemic: Challenges and opportunities. Children and Youth Services Review, 131, 106297. 10.1016/j.childyouth.2021.106297 [DOI] [Google Scholar]
  18. Funder DC, & Ozer DJ (2019). Evaluating effect size in psychological research: Sense and nonsense. Advances in Methods and Practices in Psychological Science, 2, 156–168. 10.1177/2515245919847202 [DOI] [Google Scholar]
  19. Fram MS, Kim J, & Sinha S Early care and prekindergarten care as influences on school readiness (2012). Journal of Family Issues, 33(4), 478–505. 10.1177/0192513X11415354 [DOI] [Google Scholar]
  20. Friedman-Krauss AH, Barnett WS, Garver KA, Hodges KS, Weisenfeld G, Gardiner BA, & Jost TM (2022). The state of preschool 2021: State preschool yearbook. New Brunswick, NJ: National Institute for Early Education Research. [Google Scholar]
  21. Gelman A, Hill J, & Vehtari A (2021). Regression and other stories. Cambridge University Press: Cambridge, United Kingdom. [Google Scholar]
  22. Gibbs L, Nursey J, Cook J, Ireton G, Alkemade N, Roberts M, Gallagher HC, Bryant R, Block K, Molyneaux R, & Forbes D (2019). Delayed disaster impacts on academic performance of primary school children. Child Development, 90(4), 1402–1412. 10.1111/cdev.13200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Golinkoff RM, Hoff E, Rowe ML, Tamis-LeMonda CS, & Hirsh-Pasek K (2019). Language matters: Denying the existence of the 30-million-word gap has serious consequences. Child Development, 90(3), 985–992. 10.1111/cdev.13128 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hahn RA, Rammohan V, Truman BI, Milstein B, Johnson RL, Muntañer C, … & Community Preventive Services Task Force. (2014). Effects of full-day kindergarten on the long-term health prospects of children in low-income and racial/ethnic-minority populations: A community guide systematic review. American Journal of Preventive Medicine, 46(3), 312–323. 10.1016/j.amepre.2013.12.003 [DOI] [PubMed] [Google Scholar]
  25. Harris D, & Larsen M (2019). The effects of the New Orleans post-Katrina market-based school reforms on medium-term student outcomes. Education Research Alliance for New Orleans. Retrieved from educationresearchalliancenola.org/files/publications/Harris-Larsen-Reform-Effects-2019-08-01.pdf [Google Scholar]
  26. Jung K & Barnett WS (2021). Impacts of the pandemic on young children and their parents: Initial findings from NIEER’s May-June 2021 Preschool Learning Activities Survey. New Brunswick, NJ: National Institute for Early Education Research. [Google Scholar]
  27. Kelley B, Weyer M, McCann M, Broom S, & Keily T (2020, Sept 28). 50-state comparison: State K-3 policies. Education Commission of the States. Retrieved from www.ecs.org/kindergarten-policies/ [Google Scholar]
  28. Kuhfeld M, Soland J, & Lewis K (2022). Test score patterns across three COVID-19-impacted school years. Educational Researcher, 51(7), 500–506. 10.3102/0013189X221109178 [DOI] [Google Scholar]
  29. Lee PI, Hu YL, Chen PY, Huang YC, & Hsueh PR (2020). Are children less susceptible to COVID-19?. Journal of Microbiology, Immunology, and Infection, 53(3), 371. 10.1016/j.jmii.2020.02.011 [DOI] [Google Scholar]
  30. Lenhard W & Lenhard A (2014). Hypothesis tests for comparing correlations. Psychometrica. 10.13140/RG.2.1.2954.1367 [DOI] [Google Scholar]
  31. Lynch K, Lee M, & Loeb S (2023). An investigation of Head Start preschool children’s executive function, early literacy, and numeracy learning in the midst of the COVID-19 pandemic. Early Childhood Research Quarterly, 64(3), 255–265. 10.1016/j.ecresq.2023.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Martin A, Miller EB, Zhong D, Gross R, Morris-Perez PA, Shaw DS, Piccolo LR, Hill J, Scott MA, Messito MJ, Canfield C, Roby E, O’Connell L, Sadler R, Aviles AI, Krug CW, Kim C, Gutierrez J, Hunter L, & Mendelsohn AL Parental cognitive stimulation of young children before and after COVID-19: Combining multiple samples with low incomes across three cities. Manuscript submitted for publication.
  33. Masten AS, & Cicchetti D (2010). Developmental cascades. Development and Psychopathology, 22(3), 491–495. 10.1017/S0954579410000222 [DOI] [PubMed] [Google Scholar]
  34. Mazzocco MM, Burchinal MR, Schulte AC, Vandell DL, Sanabria A, Hwang JK, & Connor CM (2024). Mathematics in U.S. preschool and kindergarten classrooms. Early Childhood Research Quarterly, 69(4), 25–37. 10.1016/j.ecresq.2024.04.007 [DOI] [Google Scholar]
  35. McLaughlin AE, Campbell FA, Pungello EP, & Skinner M (2007). Depressive symptoms in young adults: The influences of the early home environment and early educational child care. Child Development, 78(3), 746–756. 10.1111/j.1467-8624.2007.01030.x [DOI] [PubMed] [Google Scholar]
  36. Mendelsohn AL, Dreyer BP, Flynn V, Tomopoulos S, Rovira I, Tineo W, … & Nixon AF (2005). Use of videotaped interactions during pediatric well-child care to promote child development: A randomized, controlled trial. Journal of Developmental and Behavioral Pediatrics, 26(1), 34–41. 10.1097/DBP.0b013e3180324d87 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Miller EB, Canfield CF, Roby E, Wippick H, Shaw DS, Mendelsohn AL, & Morris-Perez PA (2024a). Enhancing early language and literacy skills for racial/ethnic minority children with low incomes through a randomized clinical trial: The mediating role of cognitively stimulating parent-child interactions. Child Development. Advance online publication. 10.1111/cdev.14064 [DOI] [Google Scholar]
  38. Miller EB, Canfield CF, Aviles AI, Hunter L, Burns J, Shaw DS, Mendesohn AL, & Morris-Perez PA (2024b). COVID-19 school disruptions in early childhood education and children’s early elementary school outcomes: Evidence from the Smart Beginnings RCT. 2024 APPAM Fall Research Conference. Retrieved 17 July, 2025 from https://www.appam.org/assets/1/6/_2024_APPAM_Program_-_Printable.pdf [Google Scholar]
  39. Miller EB, Roby E, Zhang Y, Coskun L, Rosas JM, Scott MA, Gutierrez J, Shaw DS, Mendelsohn AL, & Morris-Perez PA (2023). Promoting cognitive stimulation in low-income parents across infancy and toddlerhood: A randomized clinical trial. The Journal of Pediatrics, 225, 159–165. 10.1016/j.jpeds.2022.11.013 [DOI] [Google Scholar]
  40. Muñoz-Sandoval AF, Woodcock RW, McGrew KS, & Mather N (2005). Batería III Woodcock-Muñoz: Pruebas de Aprovechamiento. Rolling Meadows, IL: Riverside. [Google Scholar]
  41. Muthén LK and Muthén BO (1998–2003). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén. [Google Scholar]
  42. Nevo E (2023). The effect of the COVID-19 pandemic on low SES kindergarteners’ language abilities. Early Childhood Education Journal, 52, 459–469. 10.1007/s10643-023-01444-4 [DOI] [Google Scholar]
  43. Orfield G (1980). School Segregation and Residential Segregation. In: Stephan WG, Feagin JR (Eds.) School Desegregation. Springer, Boston, MA. 10.1007/978-1-4615-9155-9_10 [DOI] [Google Scholar]
  44. Parolin Z, & Lee EK (2021). Large socio-economic, geographic and demographic disparities exist in exposure to school closures. Nature Human Behavior, 5, 522–528. 10.1038/s41562-021-01087-8 [DOI] [Google Scholar]
  45. PlayReadVIP. (n.d.). Retrieved 09 September, 2024 from https://www.playreadvip.org/
  46. Pontón MO & León-Carrión J (2001). Neuropsychology and the Hispanic patient: A clinical handbook. Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]
  47. Prime H, Wade M, & Browne DT (2020). Risk and resilience in family well-being during the COVID-19 pandemic. American Psychologist, 75(5), 631–643. 10.1037/amp0000660 [DOI] [PubMed] [Google Scholar]
  48. Raikes H, Alexander Pan B, Luze G, Tamis-LeMonda CS, Brooks-Gunn J, Constantine J, Tarullo LB, Raikes HA, & Rodriguez ET (2006). Mother–child bookreading in low-income families: Correlates and outcomes during the first three years of life. Child Development, 77(4), 924–953. 10.1111/j.1467-8624.2006.00911.x [DOI] [PubMed] [Google Scholar]
  49. Reardon SF, & Portilla XA (2016). Recent trends in income, racial, and ethnic school readiness gaps at kindergarten entry. AERA Open, 2(3), 1–18. 10.1177/2332858416657343 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Rollnick S, & Miller WR (1995). What is motivational interviewing? Behavioural and Cognitive Psychotherapy, 23(4), 325–334. 10.1017/S135246580001643X [DOI] [Google Scholar]
  51. Rubens SL, Felix ED, & Hambrick EP (2018) A meta-analysis of the impact of natural disasters on internalizing and externalizing problems in youth. Journal of Traumatic Stress, 31(3), 332–341. 10.1002/jts.22292 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Sandler I, Wolchik S, Mazza G, Gunn H, Tein J-Y, Berkel C, Jones S, & Porter M (2019). Randomized effectiveness trial of the New Beginnings Program for divorced families with children and adolescents. Journal of Clinical Child & Adolescent Psychology, 49(1), 60–78. 10.1080/15374416.2018.1540008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Scarborough HS (2001). Connecting early language and literacy to later reading (dis)abilities: Evidence, theory, and practice. In Neuman S & Dickinson D (Eds.), Handbook for research in early literacy (pp. 97–110). New York: Guilford Press. [Google Scholar]
  54. Schrank FA, Mather N, & McGrew KS (2014a). Woodcock-Johnson IV Tests of Achievement. Rolling Meadows, IL: Riverside. [Google Scholar]
  55. Schrank FA, Mather N, & McGrew KS (2014a). Woodcock-Johnson IV Tests of Oral Language. Rolling Meadows, IL: Riverside. [Google Scholar]
  56. Shaw DS, Mendelsohn AL & Morris PA (2021). Reducing poverty-related disparities in child development and school readiness: The Smart Beginnings tiered prevention strategy that combines pediatric primary care with home visiting. Clinical Child and Family Psychology Review, 24, 669–683. 10.1007/s10567-021-00366-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. StataCorp. (2023). Stata Statistical Software: Release 18. College Station, TX: StataCorp LLC. [Google Scholar]
  58. Steinberg Daniel. (2023). Mayor’s management report. Retrieved from www.nyc.gov/assets/operations/downloads/pdf/mmr2023/2023_mmr.pdf
  59. Stormont M, Cohen DR, Herman KC, & Reinke WM (2019). Teacher-rated school readiness items in a kindergarten sample: Outcomes in first grade. School Psychology, 34(6), 612–621. 10.1037/spq0000329 [DOI] [PubMed] [Google Scholar]
  60. Swenson CC, Saylor CF, Powell MP, Stokes SJ, Foster KY, & Belter RW (1996). Impact of a natural disaster on preschool children: Adjustment 14 months after a hurricane. American Journal of Orthopsychiatry, 66(1), 122–130. 10.1037/h0080162 [DOI] [PubMed] [Google Scholar]
  61. Torgesen JK, Wagner RK, & Rashotte CA (2012). Test of Word Reading Efficiency, Second Edition. Austin, TX: PRO-ED, Inc. [Google Scholar]
  62. U.S. Department of Education. (2025). Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), The Nation’s Report Card. [Google Scholar]
  63. Van Lancker W, & Parolin Z (2020). COVID-19, school closures, and child poverty: A social crisis in the making. The Lancet Public Health, 5(5), e243–e244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Weiland C, Greenberg E, Bassok D, Markowtiz A, Rosada PG, Luetmer G, Abenavoli R, Gomez C, Johnson A, Jones-Harden B, Maier M, McCormick M, Morris P, Nores M, Phillips D, & Snow C (2021). Historic crisis, historic opportunity: Using evidence to mitigate the effects of the COVID-19 crisis on young children and early care and education programs. Retrieved from https://edpolicy.umich.edu/sites/epi/files/2021-07/EPI-UI-Covid%20Synthesis%20Brief%20June%202021.pdf
  65. Weiland C, & Yoshikawa H (2013). Impacts of a prekindergarten program on children’s mathematics, language, literacy, executive function, and emotional skills. Child Development, 84, 2112–2130. 10.1111/cdev.12099 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

De-identified data will be made available to interested researchers through the establishment of data sharing agreements. Data will be made available to researchers who provide a methodologically sound proposal for use in achieving the goals of the approved proposal. Proposals should be submitted to Pamela Morris-Perez (pamela.morris@nyu.edu).

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