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Combined Language and Code Emergent Literacy Intervention for At-Risk Preschool Children: A Systematic Meta-Analytic Review

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Title: Combined Language and Code Emergent Literacy Intervention for At-Risk Preschool Children: A Systematic Meta-Analytic Review
Language: English
Authors: Jennie Cusiter (ORCID 0000-0003-3033-5365), Kate Short (ORCID 0000-0002-2022-0620), Annabel Webb (ORCID 0000-0001-8435-4436), Natalie Munro (ORCID 0000-0002-5870-6378)
Source: Child Development. 2025 96(4):1519-1545.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 27
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Descriptors: Emergent Literacy, Meta Analysis, At Risk Students, Preschool Children, Intervention, Child Language, Vocabulary Development, Phonological Awareness, Language Skills, Outcomes of Education
DOI: 10.1111/cdev.14252
ISSN: 0009-3920
1467-8624
Abstract: This meta-analytic review explored the characteristics and effectiveness of combined language (e.g., vocabulary) and code (e.g., phonological awareness) interventions, including synergistic intervention effects for at-risk preschoolers. Data from 29 randomized controlled trials, published before March 2023, reporting on 43 interventions, including 9333 children (4-6 years; 55% male, 45% African American, 30% Hispanic) were included in the meta-analyses. Composite intervention effects were small: language (g = 0.11) and code (g = 0.23). Language and code outcomes were significantly related (p = 0.032). Interventions equally targeting code and language subskills produced equivalent or greater code and language outcomes than those with an unequal emphasis. Implications for future combined intervention studies are discussed.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1475426
Database: ERIC
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  Value: <anid>AN0186282420;cdv01jul.25;2025Jul02.02:52;v2.2.500</anid> <title id="AN0186282420-1">Combined Language and Code Emergent Literacy Intervention for At‐Risk Preschool Children: A Systematic Meta‐Analytic Review </title> <p>This meta‐analytic review explored the characteristics and effectiveness of combined language (e.g., vocabulary) and code (e.g., phonological awareness) interventions, including synergistic intervention effects for at‐risk preschoolers. Data from 29 randomized controlled trials, published before March 2023, reporting on 43 interventions, including 9333 children (4–6 years; 55% male, 45% African American, 30% Hispanic) were included in the meta‐analyses. Composite intervention effects were small: language (g = 0.11) and code (g = 0.23). Language and code outcomes were significantly related (p = 0.032). Interventions equally targeting code and language subskills produced equivalent or greater code and language outcomes than those with an unequal emphasis. Implications for future combined intervention studies are discussed.</p> <p>Keywords: at‐risk; early literacy; emergent literacy development; intervention; language disorder; preschool</p> <hd id="AN0186282420-2">Introduction</hd> <p>Literacy acquisition in the early years of schooling is a critical milestone. How well children read and comprehend written information directly impacts their academic, employment, and life outcomes (Catts et al. [<reflink idref="bib9" id="ref1">9</reflink>]; Law et al. [<reflink idref="bib36" id="ref2">36</reflink>]). The Programme for International Student Assessment (PISA) reported that 23% of 15‐year‐old children assessed in 2018 had significantly low literacy levels that directly impacted their ability to participate in life situations (Thomson et al. [<reflink idref="bib64" id="ref3">64</reflink>]). Illiteracy is a significant global issue, and focus must be directed toward approaches to reduce this problem.</p> <hd id="AN0186282420-3">Theoretical Frameworks for Reading</hd> <p>The Simple View of Reading (Gough and Tunmer [<reflink idref="bib21" id="ref4">21</reflink>]) is a widely accepted theoretical framework (Lonigan et al. [<reflink idref="bib39" id="ref5">39</reflink>]) explaining that reading comprehension, the ultimate aim of reading, is the product of decoding and linguistic comprehension skills working together. Decoding refers to the capacity to identify a written word, while linguistic comprehension is the ability to understand it (Gough and Tunmer [<reflink idref="bib21" id="ref6">21</reflink>]). These two constructs represent numerous code and language subskills that interact in a complex process, which is reading. Kim ([<reflink idref="bib30" id="ref7">30</reflink>]) proposed the Direct and Indirect Effect Model of Reading (DIER) to reflect this complexity better. Testing of the model confirmed the hierarchical and interactive relationships between the many code, language, and executive function subskills required to read (Kim [<reflink idref="bib30" id="ref8">30</reflink>], [<reflink idref="bib31" id="ref9">31</reflink>]). The DIER includes the code subskills of phonological awareness and orthography (including letter knowledge) and the language subskills of vocabulary, grammar, morphology, and inferencing (Kim [<reflink idref="bib31" id="ref10">31</reflink>]).</p> <p>The foundation of these code and language subskills is laid in the preschool years. Strong empirical evidence exists of a predictive relationship between preschool oral language skills and reading comprehension via linguistic comprehension (Chiu [<reflink idref="bib10" id="ref11">10</reflink>]; Hjetland et al. [<reflink idref="bib26" id="ref12">26</reflink>]; Shanahan and Lonigan [<reflink idref="bib61" id="ref13">61</reflink>]). The specific oral language subskills include vocabulary, grammar, narrative, and inferencing (Chiu [<reflink idref="bib10" id="ref14">10</reflink>]; Hjetland et al. [<reflink idref="bib26" id="ref15">26</reflink>]; Shanahan and Lonigan [<reflink idref="bib61" id="ref16">61</reflink>]). There is similarly strong evidence for the predictive relationship between code skills and reading comprehension via decoding (Chiu [<reflink idref="bib10" id="ref17">10</reflink>]; Melby‐Lervåg et al. [<reflink idref="bib45" id="ref18">45</reflink>]). The specific code subskills include alphabet knowledge, phonological awareness (including phoneme awareness and rhyme), and print awareness (Chiu [<reflink idref="bib10" id="ref19">10</reflink>]; Shanahan and Lonigan [<reflink idref="bib61" id="ref20">61</reflink>]). These emergent literacy subskills provide a unique and predictive window into a preschool child's reading future.</p> <hd id="AN0186282420-4">Code and Language Difficulties in Children Experiencing Social Adversity</hd> <p>Significantly, many children arrive at school with low emergent literacy levels in either oral language, code subskills, or both. In a longitudinal study of Australian children, 20.6% of 4‐year‐olds met the criteria for low language levels (Reilly et al. [<reflink idref="bib57" id="ref21">57</reflink>]). This proportion increases to 40% when assessing children who experience social adversity or who develop in low socioeconomic environments (Law et al. [<reflink idref="bib35" id="ref22">35</reflink>]). Preschool children with delayed oral language development are six times more likely to have literacy difficulties at school than their peers with typical language development (Catts et al. [<reflink idref="bib9" id="ref23">9</reflink>]). Preschool code subskill acquisition follows a similar gradient. Smith et al. ([<reflink idref="bib62" id="ref24">62</reflink>]) found that 76.7% of children with low language levels from a socially vulnerable population also had code‐related difficulties naming letters and identifying sounds. Preschool language and code difficulties, and thereby subsequent literacy difficulties, disproportionately affect children who experience social adversity, such as those who live in households with reduced income, low levels of maternal education, single parenthood, or poor parental mental and physical health (Law et al. [<reflink idref="bib35" id="ref25">35</reflink>]; Smith et al. [<reflink idref="bib62" id="ref26">62</reflink>]). The case for preventive reading intervention in the preschool years is evident (Shanahan and Lonigan [<reflink idref="bib61" id="ref27">61</reflink>]). The predictive relationship between the underpinning preschool code and language subskills to later reading comprehension, the contribution of these foundational subskills in later reading via the DIER model, and the high prevalence of comorbid code and language difficulties in preschool children suggest a combined language and code emergent literacy intervention could be the answer (Kim [<reflink idref="bib30" id="ref28">30</reflink>]; Lonigan et al. [<reflink idref="bib43" id="ref29">43</reflink>]).</p> <hd id="AN0186282420-5">The Rationale for Combined Intervention</hd> <p>Combined language and code emergent literacy interventions offer a logical approach to mitigating later reading failure. The complex interaction between language and code subskills illustrated by the Lexical Restructuring Model (LRM) theory proposed by Metsala and Walley ([<reflink idref="bib46" id="ref30">46</reflink>]) adds to this position. Metsala and Walley ([<reflink idref="bib46" id="ref31">46</reflink>]) posited that vocabulary and phonemic awareness development are intrinsically linked. That is, phoneme awareness implicitly develops in early childhood through the accumulation of perceptual representations of spoken words, which is related to the rate and density of vocabulary learning (Metsala and Walley [<reflink idref="bib46" id="ref32">46</reflink>]). This implicit perceptual awareness then influences the development of phonemic awareness skills required for reading (Metsala and Walley [<reflink idref="bib46" id="ref33">46</reflink>]). This theory has since been supported by studies demonstrating that children's vocabulary knowledge accounts for unique variance in the phonological awareness subskills of blending, elision, and initial sound recognition (Hipfner‐Boucher et al. [<reflink idref="bib25" id="ref34">25</reflink>]; Hogan [<reflink idref="bib27" id="ref35">27</reflink>]). Hipfner‐Boucher et al. ([<reflink idref="bib25" id="ref36">25</reflink>]) extended this by finding that the complex oral language skill of narrative discourse contributed to phonological awareness skills more than the contribution of vocabulary. As narrative discourse draws upon multiple linguistic domains, these results highlight the need for a broader range of oral language subskills to be considered beyond that of vocabulary.</p> <hd id="AN0186282420-6">Potential Benefits and Risks of Combined Intervention</hd> <p>Translating these theories and findings to emergent literacy intervention suggests that simultaneously targeting oral language and code subskills could be reciprocally beneficial. Lonigan et al. ([<reflink idref="bib43" id="ref37">43</reflink>]) referred to these potential benefits as synergistic intervention effects. However, the few studies that have examined synergistic intervention effects between code and language skills in at‐risk children have found mixed results. Lonigan et al. ([<reflink idref="bib43" id="ref38">43</reflink>]) and Gettinger and Stoiber ([<reflink idref="bib20" id="ref39">20</reflink>]) explored the possibility of synergistic intervention effects by comparing combined emergent literacy intervention to domain‐specific code and language intervention conditions. They found that intervention effects were only observed for the code and language subskills targeted and that the combined intervention produced similar vocabulary and phonological awareness subskill effects to the interventions with a domain‐specific focus (Gettinger and Stoiber [<reflink idref="bib20" id="ref40">20</reflink>]; Lonigan et al. [<reflink idref="bib43" id="ref41">43</reflink>]). Aram ([<reflink idref="bib2" id="ref42">2</reflink>]) found enhanced vocabulary effects when vocabulary intervention was combined with an alphabetic skills program. Interestingly, Gettinger and Stoiber ([<reflink idref="bib20" id="ref43">20</reflink>]) and Aram ([<reflink idref="bib2" id="ref44">2</reflink>]) found that alphabet knowledge performed better in domain‐specific intervention than in the combined condition. It is worth noting, however, that cumulative intervention time between the combined and domain‐specific interventions was held constant across all three studies. This meant that the combined interventions had at least half the language and code dosage of the domain‐specific interventions but managed to produce similar intervention effects for vocabulary and phonological awareness outcomes. This raises two important considerations. Firstly, code and language subskills require explicit focus to promote skill acquisition and are thereby both needed in a preventative emergent literacy intervention. Secondly, combined interventions will inherently reduce the dosage time for each subskill. As dosage is a critical moderator of intervention effectiveness (Frizelle et al. [<reflink idref="bib17" id="ref45">17</reflink>]), the question remains: can potential synergistic benefits of multi‐target combined interventions overcome the reduction in specific subskill dosage?</p> <p>Our systematic meta‐analytic review seeks to collate the outcomes of high‐quality combined code and oral language intervention studies to answer the following questions:</p> <p></p> <ulist> <item> What are the characteristics of combined code and oral language emergent literacy interventions for preschool children at risk of literacy difficulties?</item> <p></p> <item> Are combined code and oral language preschool interventions effective for preschool children at risk of literacy difficulties?</item> <p></p> <item> Is there evidence of a synergistic relationship between code and language outcomes from a combined intervention?</item> </ulist> <hd id="AN0186282420-7">Method</hd> <p></p> <hd id="AN0186282420-8">Protocol Registration and Reporting Standards</hd> <p>The study protocol for this systematic meta‐analytic review was registered with the PROSPERO registry (CRD: 42018114154) and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA; Page et al. [<reflink idref="bib49" id="ref46">49</reflink>]).</p> <hd id="AN0186282420-9">Search Strategy</hd> <p>A systematic and comprehensive search was conducted of 10 databases: Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Education Resources Information Centre (ERIC), PsychINFO, SpeechBITE, Exerpta Medica database (EMBASE), Allied and Complementary Medicine Database (AMED), Linguistics and Language Behavior Abstracts (LLBA), Cochrane Database of Systematic Reviews (CDSR), and Scopus. The search included studies published up until 7th March 2023.</p> <p>The search strategy focused on identifying studies delivering combined code and language intervention. Combined intervention terms included "emergent literacy" or "school readiness" or "pre‐literacy" or "shared book reading" as these intervention types frequently include both code and language targets. The strategy also included a combination of language‐ and code‐specific skills to ensure all relevant studies were identified; "language" or "vocabulary" or "semantics" or "grammar" or "syntax" or "narrative" combined with "phonological awareness" or "phonemic awareness" or "alphabet knowledge" or "letter knowledge" or "phonics". See the Data S1 for the full search strategy.</p> <hd id="AN0186282420-10">Inclusion Criteria</hd> <p>Study eligibility was determined using the following inclusion criteria:</p> <p></p> <ulist> <item> The study included a combined code and language intervention that simultaneously targeted at least one language subskill, such as vocabulary or grammar, and one code subskill, such as letter knowledge or phonological awareness. For this review, the intervention included any program or treatment provided in addition to standard preschool curricula that specifically targeted code and language subskills. The intervention could have been provided directly to an individual child or a group of children and by any interventionist, including a preschool teacher, speech‐language pathologist, or parent, in any intervention setting such as a home, preschool, or a clinic.</item> <p></p> <item> The study reported a quantitative outcome for at least one language and one code subskill immediately pre‐ and post‐treatment, of which pre‐ and post‐means, standard deviations, and effect sizes could be determined.</item> <p></p> <item> At least 50% of the study participants had to be preschool children between 4 and 6 years of age, yet to begin formal schooling, and who were identified with a speech or language disorder or both or at risk for literacy failure based on their socioeconomic status (SES) or academic scores. SES was determined based on eligibility for free lunches, reported familial income falling below the poverty line, or attending a program with low‐income entry criteria such as Head Start (U.S. Department of Health and Human Services [<reflink idref="bib65" id="ref47">65</reflink>]).</item> <p></p> <item> The study was not focused on children diagnosed with a neuro‐developmental disorder (e.g., autism) or a diagnosed disability (e.g., Down syndrome).</item> <p></p> <item> The study design was a randomized control trial published in a peer‐reviewed journal. Participants had to be randomly assigned to intervention, control, or a comparator, individually or as part of a cluster. Control groups included business‐as‐usual, such as standard preschool curriculum, waitlist, or no intervention. Comparator interventions had to have a different developmental focus, such as mathematics, or have a domain‐specific language or code subskill focus. Where studies included both a control and comparator, control groups were selected for the meta‐analysis. Where multiple comparators were reported, the comparator furthest from the intervention of interest was selected.</item> <p></p> <item> Studies had to be published in English or translated into English.</item> </ulist> <p>Blinded dual screening of title and abstract (κ = 0.61) and full text (κ = 0.98) was conducted by the first and second authors, with consensus obtained on any discrepancies and conflicts resolved by the fourth author. Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia), available at www.covidence.org, was used to manage the review process, and Microsoft Excel was used to manage data extraction. The first author completed data extraction including: (a) population characteristics of sex, ethnicity, age, and at‐risk determination; (b) intervention characteristics such as intervention setting, targets, dose, and fidelity; and (c) quantitative code and language outcome measures. The second author completed the reliability of data extraction on 4/29 studies, with 98% agreement. Disagreements were resolved via consensus. We contacted 16 study authors to request missing data: four provided positive responses.</p> <p>The interventions were coded as either distributed or explicit. Distributed interventions were delivered across the preschool day and year, with no specific session number or time reported. Explicit interventions reported a more defined intervention time and use of specific session plans, a prescribed number of sessions, and specific session time. The interventions were also coded to reflect whether they had a greater emphasis on code subskills (code) or language subskills (language) or if they reported equal emphasis on both (even). For example, if an intervention targeted general oral language skills with no specific subskill detail and targeted many specific code subskills such as rhyme, print awareness, initial sound identification, and letter to sound rules, they were categorized as having a code emphasis. Alternatively, if the intervention targeted a range of specific language subskills such as vocabulary, narrative, and grammar and fewer code subskills such as print concepts, they were categorized as having a language emphasis. Even interventions reported equal emphasis on both code and language subskills. The second author completed blinded reliability of coding on 7/43 interventions, with 100% agreement.</p> <p>The quality of the studies was evaluated using the Physiotherapy Evidence Database (PEDro) scale (PEDro Scale [<reflink idref="bib50" id="ref48">50</reflink>]). The PEDro scale consists of 11 items relating to aspects of methodological quality. Each study received a score out of 10. Methodological quality was interpreted as < 4 = poor, 4–5 = fair, 6–8 = good, and 9–10 = excellent (Cashin and McAuley [<reflink idref="bib8" id="ref49">8</reflink>]).</p> <hd id="AN0186282420-11">Meta‐Analytic Strategy</hd> <p>For each of the included interventions, the pre‐ and post‐test means and standard deviations for the intervention and control groups were extracted for vocabulary, alphabet knowledge, print awareness, and phonological awareness subskills.</p> <p>Vocabulary: included both receptive and expressive vocabulary outcomes. As effect sizes for proximal vocabulary measures are known to be larger than distal vocabulary measures, and there is no relationship between proximal and distal vocabulary outcomes, these were evaluated separately in the meta‐analysis (Marulis and Neuman [<reflink idref="bib44" id="ref50">44</reflink>]; Rogde et al. [<reflink idref="bib58" id="ref51">58</reflink>]).</p> <p>Alphabet knowledge: included both letter‐to‐sound and letter knowledge outcomes. Where studies reported both, a composite alphabet knowledge outcome was calculated by finding a weighted average of the effect sizes.</p> <p>Print awareness: included outcomes relating to print concepts and print knowledge.</p> <p>Phonological awareness: included phonological awareness composite outcomes, initial sound identification, alliteration, elision, and blending outcomes because of their strong predictive relationship to later word reading skills (Melby‐Lervåg et al. [<reflink idref="bib45" id="ref52">45</reflink>]).</p> <p>Code and language composite effects were calculated for each study by taking a weighted average of the effect sizes of all code and language subskill outcomes reported (see Data S3 for a full list of code and language outcomes used to create the composite effects). Where studies reported a language or code composite, such as the Clinical Evaluation of Language Fundamental‐Preschool Second Edition (CELF‐P2): Expressive Language Index (Wiig et al. [<reflink idref="bib70" id="ref53">70</reflink>]), this was used as the composite measure. Care was taken to only represent subtests once within the composite outcome, for example, using a combination of the CELF‐P2 receptive and expressive index scores, not including the core language composite.</p> <p>Multi‐level random effects models were used to analyze any subskill where the meta‐analysis included comparisons of multiple interventions against a shared control from the same study or where multiple outcomes from the same study were included in a single meta‐analysis model. As an example, in the phonological awareness domain, some studies reported results for alliteration, elision, and blending, and these were included individually in a single meta‐analysis model. Between‐study heterogeneity was quantified using the <emph>I</emph><sups>2</sups> statistic and the variance of the random effects term for effect size, τ<sups>2</sups>. Where significant heterogeneity was present, several subgroup meta‐analyses were planned. Firstly, studies were grouped by study quality as indicated by their PEDro score: score ≤ 4, 5, and ≥ 6. Secondly, studies were grouped by intervention type: distributed or explicit. Thirdly, clinically indicated grouping, such as expressive and receptive vocabulary. All composite and subskill effects (standard mean difference) were plotted against standard error, producing an Egger's funnel plot (Light and Pillemer [<reflink idref="bib38" id="ref54">38</reflink>]; Data S2). Visual inspection of all funnel plots was conducted to identify studies outside the symmetrical funnel as potential sources of publication bias. To ensure our results were rigorous, we conducted sensitivity analyses where these studies were included and excluded, comparing the results. Additionally, an Egger's test was used to establish the significance of publication bias for the composite code and language outcomes (Egger et al. [<reflink idref="bib14" id="ref55">14</reflink>]). Whilst for all subskill outcomes, a multilevel weighted linear regression between‐study effect sizes and standard errors was used, as the subskill outcomes contained multiple within‐study comparisons (Nakagawa et al. [<reflink idref="bib47" id="ref56">47</reflink>]).</p> <p>Evidence of a synergistic relationship was explored using a subgroup meta‐analysis of the code and language composite effects and all code and language subskills by intervention emphasis (code, language, or even emphasis). A random effects meta‐regression was also conducted, examining the relationship between the composite code and composite language effects. All analyses were performed using the 'meta' package (Harrer et al. [<reflink idref="bib23" id="ref57">23</reflink>]) in R Statistical Software (v4.3.1; R Core Team [<reflink idref="bib55" id="ref58">55</reflink>]).</p> <hd id="AN0186282420-12">Results</hd> <p></p> <hd id="AN0186282420-13">Study Characteristics</hd> <p>The systematic search identified 5692 studies once duplicates were removed (Figure 1). A total of 29 studies reporting on 43 interventions with a combined participant cohort of 9333 fulfilled eligibility criteria. Of the 29 included studies, 25 were conducted in the United States of America, and one each from Israel, Australia, Canada, and Costa Rica. Twenty studies were randomized controlled trials, and nine were pseudorandomized controlled trials. According to the PEDro scores, methodological quality was fair for 18 (62%) studies and good for 11 (38%).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0001.jpg" title="1 PRISMA flow chart (Page et al. [49])." /> </p> <p></p> <hd id="AN0186282420-15">Participant Characteristics</hd> <p>Most studies (26/29) focused on children at risk for literacy failure as indicated by their socioeconomic status, inclusion in at‐risk education programs such as Head Start, or low test scores on emergent literacy or language screening (Table 1). Three studies focused specifically on children with diagnosed speech and/or language difficulties. Half the studies reported on maternal education. On average, child participants were African American (45%), Hispanic (30%), White (20%), or other (5%). The participant cohort was 55% male.</p> <p>1 TABLE Included studies, study quality, intervention characteristics, and meta‐analysis combined effect size (ES) outcomes.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Study; Country; Study quality (PEDro score); Population</th><th align="center">Experimental and control intervention</th><th align="center">Intervention type and child interventionist</th><th align="center">Dosage & cumulative intervention time (CIT)</th><th align="center">Composite language and code ES (<italic>g</italic>) 95% CI</th></tr></thead><tbody valign="top"><tr><td align="left">Aram (<xref ref-type="bibr" rid="bibr2">2006</xref>)IsraelPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Combined alphabetic and storybook reading program (n = 40)Control: Standard Preschool Curriculum (n = 41)</td><td align="center">Type: PD/Training to Educators, Resources, Supplemental Curricula, Coaching to EducatorsChild Interventionist: Preschool Teacher</td><td align="center">50 sessions, 20–30 min each, twice weekly, over a year. CIT = 1250 min</td><td align="center">Language: 0.30 [−0.14, 0.74]Code: 1.23 [0.76, 1.71]</td></tr><tr><td align="left">Assel et al. (<xref ref-type="bibr" rid="bibr3">2007</xref>)United StatesPEDro = 4At‐Risk (Low SES)</td><td align="center">Experimental: [A] Doors to Discovery (Wright Group, McGraw‐Hill <xref ref-type="bibr" rid="bibr73">2001</xref>) + Mentoring (n = 30)Control: Unspecified Curriculum (n = 84)</td><td align="center">Type: Curriculum, PD to Educators, Coaching to Educators, Home ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day; over the school year. CIT = Not calculable</td><td align="center">Language: −0.04 [−0.45, 0.38]Code: 0.36 [−0.16, 0.89]</td></tr><tr><td align="center">Experimental: [B] Doors to Discovery (Wright Group, McGraw‐Hill <xref ref-type="bibr" rid="bibr73">2001</xref>) (n = 31)Control: Unspecified Curriculum (n = 84)</td><td align="center">Type: Curriculum, PD to Educators, Home ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day; over the school year. CIT = Not calculable</td><td align="center">Language: −0.12 [−0.53, 0.30]Code: 0.22 [−0.26, 0.70]</td></tr><tr><td align="center">Experimental: [C] Let's begin with the Letter People (Abrams & Company <xref ref-type="bibr" rid="bibr1">2001</xref>), + Mentoring (n = 32)Control: Unspecified Curriculum (n = 84)</td><td align="center">Type: Curriculum, PD to Educators, Coaching to Educators, Home ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day; over the school year. CIT = Not calculable</td><td align="center">Language: −0.03 [−0.43, 0.38]Code: 0.18 [−0.35, 0.72]</td></tr><tr><td align="center">Experimental: [D] Let's begin with the Letter People (Abrams & Company <xref ref-type="bibr" rid="bibr1">2001</xref>; n = 32)Control: Unspecified Curriculum (n = 84)</td><td align="center">Type: Curriculum, PD to Educators, Home ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day; over the school year. CIT = Not calculable</td><td align="center">Language: 0.49 [0.07, 0.90]Code: 1.10 [0.64, 1.56]</td></tr><tr><td align="left">Bailet et al. (<xref ref-type="bibr" rid="bibr5">2009</xref>)United StatesPEDro = 4At‐Risk (Low literacy/language)</td><td align="center">Experimental: Emergent Literacy Intervention (n = 99)Control: Delayed intervention (n = 86)</td><td align="center">Type: Supplemental CurriculaChild Interventionist: Preschool Teacher</td><td align="center">18 sessions, 30 min each, twice weekly, for 9 weeks. CIT = 4860 min</td><td align="center">Language: 0.18 [−0.11, 0.47]Code: 0.37 [0.08, 0.66]</td></tr><tr><td align="left">Bailet et al. (<xref ref-type="bibr" rid="bibr4">2013</xref>)United StatesPEDro = 6At‐Risk (Low literacy/language)</td><td align="center">Experimental: Emergent Literacy Intervention (n = 128)Control: Delayed intervention (n = 138)</td><td align="center">Type: Supplemental CurriculaChild Interventionist: Preschool Teacher</td><td align="center">18 sessions, 30 min each, twice weekly, for 9 weeks. CIT = 4860 min</td><td align="center">Language: 0.05 [−0.20, 0.29]Code: 0.37 [0.13, 0.61]</td></tr><tr><td align="left">Bierman et al. (<xref ref-type="bibr" rid="bibr6">2008</xref>)United StatesPEDro = 7At‐Risk (Low SES)</td><td align="center">Experimental: Head Start REDI program (Research‐based, Developmentally Informed) (n = 192)Control: High/Scope or Creative Curriculum (n = 164)</td><td align="center">Type: Supplemental Curricula, Parent Training, PD/Training to Educators, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.03 [−0.18, 0.24]Code: 0.22 [0.01, 0.43]</td></tr><tr><td align="left">Davidson et al. (<xref ref-type="bibr" rid="bibr11">2009</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Ready, Set, Leap! (RSL; n = 129)Control: High Scope Curriculum (n = 125)</td><td align="center">Type: Paper‐Based Technology, Curriculum, PD/Training to Educators, Coaching to EducatorsChild Interventionist: Preschool Teacher, Computer program</td><td align="center">60‐120min sessions, once day, 10 months. CIT = Not calculable</td><td align="center">Language: 0.06 [−0.18, 0.31]Code: 0.21 [−0.03, 0.46]</td></tr><tr><td align="left">DeBaryshe and Gorecki (<xref ref-type="bibr" rid="bibr12">2007</xref>)United StatesPEDro = 4At‐Risk (Low SES)</td><td align="center">Experimental: Learning Connections Literacy Curriculum (n = 51)Control: Head Start Curriculum (n = 30)</td><td align="center">Type: Curriculum, PD/Training to Educators, Coaching to Educators, Parent Training,ResourcesChild Interventionist: Preschool Teacher, Parents</td><td align="center">1 dialogic reading activity + small group either alphabet, print awareness or phonemic awareness activity, daily, for 7 months. CIT = Not calculable</td><td align="center">Language: 0.17 [−0.28, 0.62]Code: 0.32 [−0.13, 0.78]</td></tr><tr><td align="left">Farver et al. (<xref ref-type="bibr" rid="bibr15">2009</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Literacy Express Preschool Curriculum (English; n = 31)Control: High Scope Curriculum (n = 32)</td><td align="center">Type: Supplemental CurriculaChild Interventionist: Preschool Teacher</td><td align="center">81 sessions, 20 min each, four times a week, for 21 weeks. CIT = 1680 min</td><td align="center">Language: 0.19 [−0.30, 0.69]Code: 0.28 [−0.22, 0.78]</td></tr><tr><td align="left">Fischel et al. (<xref ref-type="bibr" rid="bibr16">2007</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: [A] Let's begin with the Letter People (Let's begin; Abrams & Company <xref ref-type="bibr" rid="bibr1">2001</xref>; n = 185)Control: High Scope Curriculum (n = 150)</td><td align="center">Type: Supplemental CurriculaChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.01 [−0.20, 0.23]Code: 0.35 [0.13, 0.56]</td></tr><tr><td align="center">Experimental: [B] Waterford Early Reading Program (Waterford; Waterford Institute, <xref ref-type="bibr" rid="bibr69">2001</xref>; n = 172)Control: High Scope Curriculum (n = 150)</td><td align="center">Type: Computer ProgramChild Interventionist: Computer program</td><td align="center">150 intended sessions (116 achieved), 15 min, once a day, over the school year. CIT = 1740 min</td><td align="center">Language: 0.05 [−0.17, 0.26]Code: 0.18 [−0.04, 0.39]</td></tr><tr><td align="left">Gettinger and Stoiber (<xref ref-type="bibr" rid="bibr19">2012</xref>)United StatesPEDro = 4At‐Risk (Low SES)</td><td align="center">Experimental: Exemplary Model of Early Reading Growth (EMERGE; n = 62)Control: Modified High Scope Curriculum (n = 62)</td><td align="center">Type: Curriculum, PD/Training to Educators, Coaching to Educators, RTI approachChild Interventionist: Preschool Teacher</td><td align="center">Whole preschool day and 30 min small group, daily, for the school year. CIT = Not calculable</td><td align="center">Language: −0.01 [−0.36, 0.34]Code: 0.45 [0.09, 0.81]</td></tr><tr><td align="left">Gettinger and Stoiber (<xref ref-type="bibr" rid="bibr20">2018</xref>)United StatesPEDro = 6At‐Risk (Low SES)</td><td align="center">Experimental: Shared Book Reading (SBR) + Code Focused Interactions + Meaning Focused Interactions (n = 8)Control: Scripted shared book reading (standard teacher talk; n = 6)</td><td align="center">Type: InterventionChild Interventionist: Trained interventionist</td><td align="center">24 sessions, 20 min each, four times weekly, for 6 weeks. CIT = 480 min</td><td align="center">Language: 1.93 [0.58, 3.28]Code: 0.60 [−0.49, 1.69]</td></tr><tr><td align="left">Griffith et al. (<xref ref-type="bibr" rid="bibr22">2019</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Education Apps (n = 11)Control: Entertainment apps (n = 11)</td><td align="center">Type: Computer Program (Aps)Child Interventionist: Computer program</td><td align="center">53 days, 38 min per day, for 3 months. CIT = 2019 min</td><td align="center">Language: 0.07 [−0.77, 0.90]Code: 0.45 [−0.39, 1.30]</td></tr><tr><td align="left">Huffstetter et al. (<xref ref-type="bibr" rid="bibr28">2010</xref>)United StatesPEDro = 6At‐Risk (Low SES)</td><td align="center">Experimental: Headsprout Early Reading (n = 31)Control: Millies Math House (n = 31)</td><td align="center">Type: Computer ProgramChild Interventionist: Computer program</td><td align="center">40 sessions, 30 min each, daily, for 8 weeks. CIT = 1200 min</td><td align="center">Language: 0.85 [0.33, 1.37]Code: 1.03 [0.50, 1.56]</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr34">2017</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: [A] The Early Education Model (TEEM) + Play and Learning Strategies (PALS; n = 102)Control: Core Curriculum (n = 111)</td><td align="center">Type: PD/Training to Educators, Coaching to Educators, Resources, Parent TrainingChild Interventionist: Preschool Teacher, Parents</td><td align="center">Whole of preschool Day and Home‐based activities, for the school year. CIT = Not calculable</td><td align="center">Language: 0.93 [0.65, 1.21]Code: 0.22 [−0.05, 0.49]</td></tr><tr><td align="center">Experimental: [B] The Early Education Model (TEEM; n = 112)Control: Business as usual curriculum (n = 111)</td><td align="center">Type: PD/Training to Educators, Coaching to Educators, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.04 [−0.23, 0.30]Code: −0.11 [−0.37, 0.16]</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr32">2019</xref>)United StatesPEDro = 5At‐Risk (Low literacy/language) and At‐Risk (Low SES)</td><td align="center">Experimental: Preparing Pequeños (n = 397)Control: Frog Street Curriculum (n = 380)</td><td align="center">Type: Supplemental CurriculaChild Interventionist: Preschool teacher</td><td align="center">90‐min sessions, four times a week, for the school year. CIT = Not calculable</td><td align="center">Language: 0.14 [−0.00, 0.28]Code: 0.31 [0.17, 0.45]</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr33">2021</xref>)United StatesPEDro = 4At‐Risk (Low SES)</td><td align="center">Experimental: [A] online The Early Education Model (eTEEM) + online Play and Learning Strategies (ePALS; n = 121)Control: Published general curriculum (n = 102)</td><td align="center">Type: Online PD/Training to Educators, Coaching to Educators, Resources, Parent TrainingChild Interventionist: Preschool Teacher, Parents</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.01 [−0.25, 0.28]Code: −0.04 [−0.30, 0.23]</td></tr><tr><td align="center">Experimental: [B] online The Early Education Model (eTEEM; n = 112)Control: Published general curriculum (n = 102)</td><td align="center">Type: Online PD/Training to Educators, Coaching to Educators, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: −0.05 [−0.32, 0.22]Code: 0.04 [−0.23, 0.31]</td></tr><tr><td align="left">Lefebvre et al. (<xref ref-type="bibr" rid="bibr37">2011</xref>)CanadaPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Shared Storybook Reading: Explicitly targeting language, phonological awareness, and print awareness (n = 10)Control: No intervention (n = 13)</td><td align="center">Type: Shared Book ReadingChild Interventionist: Preschool Teacher</td><td align="center">40 sessions, 20–30 min each, four times a week, for 10 weeks. CIT = 1000 min</td><td align="center">Language: 1.13 [0.23, 2.03]Code: 0.98 [0.10, 1.86]</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr41">2011</xref>)United StatesPEDro = 6At‐Risk (Academic difficulties)</td><td align="center">Experimental: [A] Literacy Express Preschool Curriculum (LEPC, Lonigan et al. <xref ref-type="bibr" rid="bibr40">2005</xref>) + PD workshop + in‐class mentoring (n = 284)Control: High Scope or Creative Curriculum (n = 242)</td><td align="center">Type: PD/Training to Educators, Curriculum,Coaching,ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable.</td><td align="center">Language: −0.16 [−0.33, 0.01]Code: 0.20 [0.03, 0.37]</td></tr><tr><td align="center">Experimental: [B] Literacy Express Preschool Curriculum (LEPC, Lonigan et al. <xref ref-type="bibr" rid="bibr40">2005</xref>) + PD workshop (n = 213)Control: High Scope or Creative Curriculum (n = 242)</td><td align="center">Type: PD/Training to Educators, Curriculum,ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: −0.14 [−0.32, 0.04]Code: 0.22 [0.04, 0.41]</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr43">2013</xref>)United StatesPEDro = 6At‐Risk (Low SES)</td><td align="center">Experimental: [A] Dialogic reading + phonological awareness intervention + letter knowledge (LK) intervention (n = 67)Control: High Scope or Creative Curriculum (n = 72)</td><td align="center">Type: InterventionChild Interventionist: Trained interventionist</td><td align="center">Dialogic reading: 20 min/day Oct‐Dec, 10 min/day January‐end of school year, 300 min of letter knowledge Intervention and 300 min of phonological awareness intervention. CIT = Max 2400 min</td><td align="center">Language: 0.03 [−0.31, 0.36]Code: 0.12 [−0.21, 0.46]</td></tr><tr><td align="center">Experimental: [B] Dialogic reading + phonological awareness intervention (n = 57)Control: High Scope or Creative Curriculum (n = 72)</td><td align="center">Type: InterventionChild Interventionist: Trained interventionist</td><td align="center">Dialogic reading: 20 min/day Oct‐Dec, 10 min/day January‐end of school year and cumulative 600 min phonological awareness Intervention. CIT = Max 2400 min</td><td align="center">Language: 0.35 [0.00, 0.70]Code: 0.21 [−0.14, 0.56]</td></tr><tr><td align="center">Experimental: [C] Dialogic reading + Letter Knowledge intervention (n = 64)Control: High Scope or Creative Curriculum (n = 72)</td><td align="center">Type: InterventionChild Interventionist: Trained interventionist</td><td align="center">Dialogic reading: 20 min/day Oct‐Dec, 10 min/day January‐end of school year and cumulative 600 min letter knowledge intervention. CIT = Max 2400 min</td><td align="center">Language: 0.35 [0.01, 0.69]Code: 0.24 [−0.10, 0.58]</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr42">2015</xref>)United StatesPEDro = 6At‐Risk (Low SES)</td><td align="center">Experimental: [A] Combined Literacy Express Curriculum (LEC; Lonigan, et al. <xref ref-type="bibr" rid="bibr40">2005</xref>), Pre‐K Math, and Promoting Alternative Thinking Strategies‐PATHS (Domitrovich et al. <xref ref-type="bibr" rid="bibr13">1999</xref>; n = 320)Control: Existing curriculum (n = 160)</td><td align="center">Type: Curriculum, PD/Training to Educators, Coaching to Educators, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: −0.00 [−0.19, 0.19]Code: 0.12 [−0.07, 0.31]</td></tr><tr><td align="center">Experimental: [B] LEC, Pre‐K Math and Implicit Social Emotional Teaching (n = 328)Control: Existing curriculum (n = 160)</td><td align="center">Type: Curriculum, PD/Training to Educators, Coaching to Educators, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: −0.00 [−0.19, 0.19]Code: 0.13 [−0.06, 0.32]</td></tr><tr><td align="left">Piasta et al. (<xref ref-type="bibr" rid="bibr52">2022</xref>)United StatesPEDro = 6At‐Risk (Low literacy/language)</td><td align="center">Experimental: [A] Nemours BrightStart!‐ Teacher Implemented (n = 101)Control: BAU: Creative Curriculum (n = 86)</td><td align="center">Type: InterventionChild Interventionist: Preschool Teacher</td><td align="center">Planned: 20 lessons, 20–30 min each, twice weekly, for 20 weeks. Received: children received m = 5.52 lessons (SD = 6.10). CIT = 1000 min</td><td align="center">Language: −0.03 [−0.32, 0.26]Code: −0.14 [−0.43, 0.15]</td></tr><tr><td align="center">Experimental: [B] Nemours BrightStart!‐ Community Aide Implemented (n = 94)Control: BAU: Creative Curriculum (n = 96)</td><td align="center">Type: InterventionChild Interventionist: Community Aide</td><td align="center">Planned: 20 lessons, 20–30 min each, twice weekly, for 20 weeks. Received: children received m = 12 lessons (SD = 6.04). CIT = 1000 min</td><td align="center">Language: 0.05 [−0.24, 0.35]Code: 0.06 [−0.23, 0.36]</td></tr><tr><td align="left">Powell et al. (<xref ref-type="bibr" rid="bibr54">2010</xref>)United StatesPEDro = 6At‐Risk (Low SES)</td><td align="center">Experimental: [A] Classroom Links to Early Literacy (On‐site PD; n = 303)Control: Wait list control (n = 259)</td><td align="center">Type: Coaching to Educators, PD/Training to EducatorsChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.01 [−0.15, 0.18]Code: 0.20 [0.03, 0.36]</td></tr><tr><td align="center">Experimental: [B] Classroom Links to Early Literacy (Remote PD; n = 306)Control: Wait list control (n = 259)</td><td align="center">Type: Coaching to Educators, PD/Training to EducatorsChild Interventionist: Preschool Teacher</td><td align="center">Whole of preschool day, for the school year. CIT = Not calculable</td><td align="center">Language: 0.08 [−0.09, 0.24]Code: 0.22 [0.05, 0.38]</td></tr><tr><td align="left">Rolla San Francisco et al. (<xref ref-type="bibr" rid="bibr59">2006</xref>)Costa RicaPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Classroom intervention + Family intervention (Project EASE‐adaption (Early Access to Success in Education, Jordan et al. <xref ref-type="bibr" rid="bibr29">2000</xref>)) + Tutoring intervention (n = 50)Control: No Intervention (n = 55)</td><td align="center">Type: Tutoring, Resources, Parent Training, InterventionChild Interventionist: Preschool Teacher, Parents</td><td align="center">Classroom intervention: 18 sessions, 45 min each, over the school year EASE: 5 parent education sessions, Tutoring: 21 sessions, 45 min each, over the school year. CIT = 1755 min</td><td align="center">Language: 0.24 [−0.15, 0.62]Code: 0.63 [0.24, 1.03]</td></tr><tr><td align="left">Schmitt et al. (<xref ref-type="bibr" rid="bibr60">2018</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: PBS KIDS Island (Web‐based game; n = 93)Control: Commercial website (n = 43)</td><td align="center">Type: Computer ProgramChild Interventionist: Computer program</td><td align="center">1 session, 15 min each, 4 times a week, over 8 weeks. CIT = 480 min</td><td align="center">Language: 0.26 [−0.10, 0.62]Code: 0.20 [−0.17, 0.56]</td></tr><tr><td align="left">Wake et al. (<xref ref-type="bibr" rid="bibr67">2013</xref>)AustraliaPEDro = 8Speech and/or Language delayed</td><td align="center">Experimental: Language for Learning intervention (n = 99)Control: Usual Care/No intervention (n = 101)</td><td align="center">Type: InterventionChild Interventionist: Trained interventionist</td><td align="center">3 × 6 session blocks each separated by 7 weeks (cumulative total 18 sessions). 1 session: 60 min, once a week over 9 months. CIT = 1080 min</td><td align="center">Language: 0.11 [−0.17, 0.38]Code: 0.29 [0.01, 0.57]</td></tr><tr><td align="left">Wilcox et al. (<xref ref-type="bibr" rid="bibr72">2011</xref>)United StatesPEDro = 4Speech and/or language delayed</td><td align="center">Experimental: Teaching Early Literacy and Language (TELL) Curriculum (n = 80)Control: BAU: Creative Curriculum, High Scope Curriculum, or an informal language curriculum (n = 38)</td><td align="center">Type: Curriculum, PD/Training to Educators, Coaching to EducatorsChild Interventionist: Preschool Teacher</td><td align="center">34 weeks instruction, sample lessons plan = 150 min length, 4 times weekly, for 34 weeks. CIT = 5100 min</td><td align="center">Language: 0.49 [0.09, 0.88]Code: 0.57 [0.17, 0.96]</td></tr><tr><td align="left">Wilcox et al. (<xref ref-type="bibr" rid="bibr71">2020</xref>)United StatesPEDro = 6Speech and/or language delayed</td><td align="center">Experimental: Teaching Early Literacy and Language (TELL) Curriculum (n = 128)Control: Business as Usual (BAU) curriculum (n = 129)</td><td align="center">Type: Curriculum, ResourcesChild Interventionist: Preschool Teacher</td><td align="center">34 weeks instruction, sample lessons plan = 150 min length, 4 times weekly, for 34 weeks. CIT = 5100 min</td><td align="center">Language: 0.13 [−0.11, 0.38]Code: 0.18 [−0.06, 0.43]</td></tr><tr><td align="left">Yazejian and Peisner‐Feinberg (<xref ref-type="bibr" rid="bibr74">2009</xref>)United StatesPEDro = 5At‐Risk (Low SES)</td><td align="center">Experimental: Supplemental music and movement curriculum (n = 115)Control: BAU: Typical Curriculum (n = 71)</td><td align="center">Type: Music Therapy, Supplemental CurriculaChild Interventionist: Preschool Teacher, Music Therapist</td><td align="center">52 sessions, 30 min each, twice a week, over 26 weeks. CIT = 1560 min</td><td align="center">Language: −0.09 [−0.39, 0.22]Code: −0.15 [−0.45, 0.15]</td></tr><tr><td align="left">Zucker et al. (<xref ref-type="bibr" rid="bibr75">2021</xref>)United StatesPEDro = 4At‐Risk (Low literacy/language) and At‐Risk (Low SES)</td><td align="center">Experimental: [A] Teaching Together: classroom/family intervention (Tier 2 Enhanced Family; n = 31)Control: BAU: Frog Street Pre‐K Curriculum or Creative Curriculum (n = 45)</td><td align="center">Type: Multitiered approach: Tier 1: Supplemental Curricula, PD/Training to Educators, Coaching to Educators, Tier 2: Resources, Lesson Plans, family coaching, home resourcesChild Interventionist: Preschool Teacher, Parents</td><td align="center">(a) 1 whole class lesson, 20 min each, 4 times per week each for 23 weeks(b) 1 small group session, 10 min each, four times a week, over 19 weeks(c) 4 individual family coaching sessions(d) 5 monthly parent–child workshops over 23 weeks. CIT = 2600 min (not including individual coaching)</td><td align="center">Language: 0.07 [−0.39, 0.52]Code: −0.34 [−0.80, 0.12]</td></tr><tr><td align="left" /></tr><tr><td align="center">Experimental: [B] Teaching Together: classroom/family intervention (Tier 2 Basic; n = 29)Control: BAU: Frog Street Pre‐K Curriculum or Creative Curriculum (n = 45)</td><td align="center">Type: Multitiered approach: Tier 1: Supplemental Curricula, PD/Training to Educators, Coaching to Educators, Tier 2: Resources, Lesson PlansChild Interventionist: Preschool Teacher, Parents</td><td align="center">(a) 1 whole class lesson, 20 min each, 4 times per week each for 23 weeks(b) 1 small group session, 10 min each, four times a week, over 19 weeks(d) 5 monthly parent–child workshops over 23 weeks. CIT = 2600 min</td><td align="center">Language: −0.08 [−0.55, 0.39]Code: −0.15 [−0.62, 0.31]</td></tr><tr><td align="center">Experimental: [C] Teaching Together: classroom/family intervention (Tier 1; n = 38)Control: BAU: Frog Street Pre‐K Curriculum or Creative Curriculum (n = 45)</td><td align="center">Type: Multitiered approach: Tier 1: Supplemental Curricula, PD/Training to Educators, Coaching to EducatorsChild Interventionist: Preschool Teacher, Parents</td><td align="center">(a) 1 whole class lesson, 20 min each, 4 times per week each for ~23 weeks(d) 5 monthly parent–child workshops over 23 weeks. CIT = 1840 min</td><td align="center">Language: 0.10 [−0.33, 0.53]Code: −0.26 [−0.69, 0.18]</td></tr></tbody></table> </ephtml> </p> <p>1 Abbreviation: PEDro = Physiotherapy Evidence Database Scale (PEDro Scale [<reflink idref="bib50" id="ref59">50</reflink>]).</p> <hd id="AN0186282420-16">Intervention Characteristics</hd> <p>Most interventions were preschool‐based (39/43), delivered by a preschool educator (32/43), trained interventionist (3/43), community aide (1/43), preschool teacher and music therapist (1/43), or via a computer (2/43; Table 1). Three interventions were delivered at home: two via a computer program and one by a trained interventionist. Within the non‐computer delivered interventions, all provided a base curriculum or program but varied by the number of additional interventionist or parent supports that accompanied the intervention. The most common intervention support was professional development to preschool educators (24/43), followed by coaching (21/43), and then the provision of program resources (13/43). Nine interventions included home resources, eight provided parent training, and one provided parent coaching. The interventions were predominantly delivered to small groups of children (32/43), with some providing individual instruction (16/43). Nearly half (47%) used more than one approach, for example, whole class and small group. Intervention fidelity was reported for 32 (74%) interventions (see Data S3 for details). All studies reported the overall duration of the intervention (43/43), with 23 (53%) reporting the total number of sessions (23/43), 27 (63%) reporting intervention frequency, and 26 (60%) reporting session length. A cumulative intervention time (multiplying session length by the total number of sessions) was calculable for 23 (53%) of the interventions. Cumulative intervention time varied from 8 h (Gettinger and Stoiber [<reflink idref="bib20" id="ref60">20</reflink>]; Schmitt et al. [<reflink idref="bib60" id="ref61">60</reflink>]) to 85 h (Wilcox et al. [<reflink idref="bib71" id="ref62">71</reflink>], [<reflink idref="bib72" id="ref63">72</reflink>]), with a mean of 36 h (SD = 25.90). Most interventions were coded as explicit (63%; Table 2).</p> <p>2 TABLE Intervention subskill targets, emphasis rating, and intervention type.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center" /><th align="center" /><th align="center">Subskill targets</th></tr><tr><th align="center">Intervention Type</th><th align="center">Emphasis Rating</th><th align="center">Language</th><th align="center">Code</th></tr></thead><tbody valign="top"><tr><td align="left">Aram (<xref ref-type="bibr" rid="bibr2">2006</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V</td><td align="center">S, M, G</td></tr><tr><td align="left">Assel et al. (<xref ref-type="bibr" rid="bibr3">2007</xref>) [A]</td><td align="center">Distributed</td><td align="center">Language</td><td align="center">V, C, O</td><td align="center">P, R, LK</td></tr><tr><td align="left">Assel et al. (<xref ref-type="bibr" rid="bibr3">2007</xref>) [B]</td><td align="center">Distributed</td><td align="center">Language</td><td align="center">V, C, O</td><td align="center">P, R, LK</td></tr><tr><td align="left">Assel et al. (<xref ref-type="bibr" rid="bibr3">2007</xref>) [C]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, R, S, Ph‐A, LK</td></tr><tr><td align="left">Assel et al. (<xref ref-type="bibr" rid="bibr3">2007</xref>) [D]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, R, S, Ph‐A, LK</td></tr><tr><td align="left">Bailet et al. (<xref ref-type="bibr" rid="bibr5">2009</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">O</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Bailet et al. (<xref ref-type="bibr" rid="bibr4">2013</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Bierman et al. (<xref ref-type="bibr" rid="bibr6">2008</xref>)</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, Gr, N</td><td align="center">P, R, S, Ph‐A, M</td></tr><tr><td align="left">Davidson et al. (<xref ref-type="bibr" rid="bibr11">2009</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">O</td><td align="center">M, LK, G</td></tr><tr><td align="left">DeBaryshe and Gorecki (<xref ref-type="bibr" rid="bibr12">2007</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr><tr><td align="left">Farver et al. (<xref ref-type="bibr" rid="bibr15">2009</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, O, Gr, N</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr><tr><td align="left">Fischel et al. (<xref ref-type="bibr" rid="bibr16">2007</xref>) [A]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, R, Ph‐A, LK</td></tr><tr><td align="left">Fischel et al. (<xref ref-type="bibr" rid="bibr16">2007</xref>) [B]</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V</td><td align="center">P, R, Ph‐A, M, LK, G</td></tr><tr><td align="left">Gettinger and Stoiber (<xref ref-type="bibr" rid="bibr19">2012</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C, O</td><td align="center">P, R, Ph‐A, M, LK, G</td></tr><tr><td align="left">Gettinger and Stoiber (<xref ref-type="bibr" rid="bibr20">2018</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr><tr><td align="left">Griffith et al. (<xref ref-type="bibr" rid="bibr22">2019</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C</td><td align="center">R, Ph‐A, LK, G</td></tr><tr><td align="left">Huffstetter et al. (<xref ref-type="bibr" rid="bibr28">2010</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C</td><td align="center">P, Ph‐A, M, G</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr34">2017</xref>) [A]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, O</td><td align="center">P, R, S, Ph‐A, M</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr34">2017</xref>) [B]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, O</td><td align="center">P, R, S, Ph‐A, M</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr32">2019</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, R, S, Ph‐A, LK</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr33">2021</xref>) [A]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, O</td><td align="center">P, R, S, Ph‐A, M</td></tr><tr><td align="left">Landry et al. (<xref ref-type="bibr" rid="bibr33">2021</xref>) [B]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, O</td><td align="center">P, R, S, Ph‐A, M</td></tr><tr><td align="left">Lefebvre et al. (<xref ref-type="bibr" rid="bibr37">2011</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr41">2011</xref>) [A]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr41">2011</xref>) [B]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr43">2013</xref>) [A]</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V</td><td align="center">R, Ph‐A, M, LK, G</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr43">2013</xref>) [B]</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V</td><td align="center">R, Ph‐A, M</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr43">2013</xref>) [C]</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V</td><td align="center">LK, G</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr42">2015</xref>) [A]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Lonigan et al. (<xref ref-type="bibr" rid="bibr42">2015</xref>) [B]</td><td align="center">Distributed</td><td align="center">Code</td><td align="center">V</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Piasta et al. (<xref ref-type="bibr" rid="bibr52">2022</xref>) [A]</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">C, O</td><td align="center">P, R, Ph‐A, M, LK, G</td></tr><tr><td align="left">Piasta et al. (<xref ref-type="bibr" rid="bibr52">2022</xref>) [B]</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">C, O</td><td align="center">P, R, Ph‐A, M, LK, G</td></tr><tr><td align="left">Powell et al. (<xref ref-type="bibr" rid="bibr54">2010</xref>) [A]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, Gr</td><td align="center">P, LK, G, Gen‐PA</td></tr><tr><td align="left">Powell et al. (<xref ref-type="bibr" rid="bibr54">2010</xref>) [B]</td><td align="center">Distributed</td><td align="center">Even</td><td align="center">V, C, Gr</td><td align="center">P, LK, G, Gen‐PA</td></tr><tr><td align="left">Rolla San Francisco et al. (<xref ref-type="bibr" rid="bibr59">2006</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V, O</td><td align="center">P, S, Ph‐A, LK</td></tr><tr><td align="left">Schmitt et al. (<xref ref-type="bibr" rid="bibr60">2018</xref>)</td><td align="center">Explicit</td><td align="center">Code</td><td align="center">V, C</td><td align="center">R, Ph‐A, LK</td></tr><tr><td align="left">Wake et al. (<xref ref-type="bibr" rid="bibr67">2013</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C, Gr, N</td><td align="center">P, R, Ph‐A</td></tr><tr><td align="left">Wilcox et al. (<xref ref-type="bibr" rid="bibr72">2011</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C, Gr</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Wilcox et al. (<xref ref-type="bibr" rid="bibr71">2020</xref>)</td><td align="center">Explicit</td><td align="center">Even</td><td align="center">V, C, Gr</td><td align="center">P, R, S, Ph‐A, M, LK, G</td></tr><tr><td align="left">Yazejian and Peisner‐Feinberg (<xref ref-type="bibr" rid="bibr74">2009</xref>)</td><td align="center">Explicit</td><td align="center">Language</td><td align="center">V, O</td><td align="center">Gen‐PA</td></tr><tr><td align="left">Zucker et al. (<xref ref-type="bibr" rid="bibr75">2021</xref>) [A]</td><td align="center">Explicit</td><td align="center">Language</td><td align="center">V, N, I</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr><tr><td align="left">Zucker et al. (<xref ref-type="bibr" rid="bibr75">2021</xref>) [B]</td><td align="center">Explicit</td><td align="center">Language</td><td align="center">V, N, I</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr><tr><td align="left">Zucker et al. (<xref ref-type="bibr" rid="bibr75">2021</xref>) [C]</td><td align="center">Explicit</td><td align="center">Language</td><td align="center">V, N, I</td><td align="center">P, R, S, Ph‐A, LK, G</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note:</emph> Distributed: Intervention dispersed across the preschool year and focused on educational enhancement, Explicit: Intervention with detailed session plans and more defined intervention time.</item> <item>3 Abbreviations: C = comprehension; Gen‐PA = general phonological awareness; Gr = grammar; G = grapheme to phoneme; I = inferencing; LK = letter knowledge; M = phoneme or syllable manipulation; N = narrative; O = oral language; P = print awareness; Ph‐A = phoneme awareness; R = rhyme; S = syllabification; V = vocabulary.</item> </ulist> <hd id="AN0186282420-17">Intervention Targets</hd> <p>The most reported language subskill target was vocabulary (39/43), followed by narrative (<reflink idref="bib6" id="ref64">6</reflink>), grammar (<reflink idref="bib6" id="ref65">6</reflink>), and inferencing (3; Table 2). Nearly half the studies targeted unspecified language areas such as oral language (<reflink idref="bib19" id="ref66">19</reflink>) and listening comprehension (<reflink idref="bib17" id="ref67">17</reflink>). Most studies targeted a phonological awareness subskill (42/43); the most common was phoneme level identification (37/43), followed by rhyme (36/43), syllabification (25/43), and phoneme or syllable manipulation (23/43). Most studies targeted alphabet knowledge (41/43) and print awareness (35/43). On average, interventions targeted five code subskills simultaneously (Table 2). Most interventions included shared book reading (35/43).</p> <hd id="AN0186282420-18">Intervention Emphasis Ratings</hd> <p>Code and language instruction was equally emphasized in 44% (<reflink idref="bib19" id="ref68">19</reflink>) of the interventions, with 42% (<reflink idref="bib18" id="ref69">18</reflink>) placing greater emphasis on code skills and 14% (<reflink idref="bib6" id="ref70">6</reflink>) placing greater emphasis on language skills (Table 2).</p> <hd id="AN0186282420-19">Effectiveness of Combined Code and Oral Language Preschool Intervention</hd> <p>Forest plots representing the results for the code and language outcomes and the code and language subskills (distal vocabulary, proximal vocabulary, alphabet knowledge, print awareness, and phonological awareness) meta‐analyses are presented in Figures 2–8. The forest plots include experimental and control sample sizes, mean changes, standard deviations, effect size, and 95% confidence interval (CI).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0002.jpg" title="2 Forest plot for composite language effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0003.jpg" title="3 Forest plot for composite code intervention effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0004.jpg" title="4 Forest plot for studies reporting distal vocabulary effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0005.jpg" title="5 Forest plot for studies reporting proximal vocabulary effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0006.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0006.jpg" title="6 Forest plot for studies reporting alphabet knowledge effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0007.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0007.jpg" title="7 Forest plot for studies reporting print awareness effects." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0008.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0008.jpg" title="8 Forest plot for studies reporting phonological awareness effects." /> </p> <p></p> <hd id="AN0186282420-27">Language Composite Intervention Effects</hd> <p>A language composite effect size was calculated for each of the 43 interventions, with data from 9356 participants. The overall mean intervention effect for composite language outcomes was very small (<emph>g</emph> = 0.11, 95% CI [0.03, 0.18], <emph>p</emph> < 0.01); however, moderate between‐study heterogeneity was present (<emph>I</emph><sups>2</sups> = 55%, τ<sups>2</sups> = 0.023, <emph>p</emph> < 0.01; Figure 2). Egger's test of publication bias was significant (<emph>p</emph> = 0.003), with four studies (Gettinger and Stoiber [<reflink idref="bib20" id="ref71">20</reflink>]; Huffstetter et al. [<reflink idref="bib28" id="ref72">28</reflink>]; Landry et al. [<reflink idref="bib34" id="ref73">34</reflink>]; Lefebvre et al. [<reflink idref="bib37" id="ref74">37</reflink>]) identified as potential sources of publication bias on the Egger's funnel plot (Data S2). A sensitivity analysis, excluding these studies, removed all between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 0%, τ<sups>2</sups> = 0.001, <emph>p</emph> = 0.52), and the mean intervention effect, while reduced, remained significant (<emph>g</emph> = 0.05, CI [0.01, 0.10], <emph>p</emph> < 0.01). A subgroup analysis by study quality was nonsignificant (<emph>p</emph> = 0.16). However, subgroup analysis by intervention type was significant (<emph>p</emph> < 0.01). Language effect was higher for explicit interventions (<emph>g</emph> = 0.12) than for distributed interventions (<emph>g</emph> = −0.01).</p> <hd id="AN0186282420-28">Code Composite Intervention Effects</hd> <p>A code composite effect size was calculated for each of the 43 interventions, with data from 9232 participants. The overall mean intervention effect for code outcomes was small (<emph>g</emph> = 0.23, 95% CI [0.15, 0.31], <emph>p</emph> < 0.01); however, moderate between‐study heterogeneity was present (<emph>I</emph><sups>2</sups> = 55%, τ<sups>2</sups> = 0.025, <emph>p</emph> < 0.01; Figure 3). Egger's test of publication bias was not significant (<emph>p</emph> = 0.14); however, visual inspection of the Egger's funnel plot (Data S2) showed obvious asymmetry, with six studies identified as sources of potential publication bias (Aram [<reflink idref="bib2" id="ref75">2</reflink>]; Assel et al. [<reflink idref="bib3" id="ref76">3</reflink>]; Gettinger and Stoiber [<reflink idref="bib20" id="ref77">20</reflink>]; Griffith et al. [<reflink idref="bib22" id="ref78">22</reflink>]; Huffstetter et al. [<reflink idref="bib28" id="ref79">28</reflink>]; Lefebvre et al. [<reflink idref="bib37" id="ref80">37</reflink>]). A sensitivity analysis, excluding these studies, accounted for some between‐study variance (<emph>I</emph><sups>2</sups> = 30%, τ<sups>2</sups> < 0.001, <emph>p</emph> = 0.05). The intervention effect was slightly reduced, but remained significant (<emph>g</emph> = 0.19, CI [0.14, 0.25], <emph>p</emph> < 0.01). Subgroup analysis by study quality was nonsignificant (<emph>p</emph> = 0.45) and did not explain any further between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 30%, τ<sups>2</sups> < 0.001, <emph>p</emph> = 0.06). Subgroup analysis by intervention type was also not statistically significant (<emph>p</emph> = 0.63) and had nil impact on residual between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 32%, τ<sups>2</sups> = 0.002, <emph>p</emph> = 0.04).</p> <hd id="AN0186282420-29">Distal Vocabulary</hd> <p>A total of 21 studies reported distal vocabulary outcomes with data from 7674 participants (Figure 4). There were 12 receptive and 25 expressive vocabulary effect sizes. The overall mean intervention effect on distal vocabulary was very small (<emph>g</emph> = 0.08, 95% CI [0.03, 0.12], <emph>p</emph> < 0.01). There was nil between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 0%, τ<sups>2</sups> < 0.001, <emph>p</emph> = 0.97). The test of publication bias was not significant (<emph>p</emph> = 0.407).</p> <hd id="AN0186282420-30">Proximal Vocabulary</hd> <p>A total of seven studies reported proximal vocabulary outcomes, with data from 1252 children (Figure 5). There were eight receptive and three expressive vocabulary effect sizes. The overall mean intervention effect on proximal vocabulary was moderate (<emph>g</emph> = 0.53, 95% CI [0.24, 0.82], <emph>p</emph> < 0.01); however, there was moderate to high between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 61%, τ<sups>2</sups> = 0.076, <emph>p</emph> < 0.01). To explore this heterogeneity, a subgroup analysis was conducted grouping receptive and expressive vocabulary effects. A statistically significant subgroup difference was found (<emph>p</emph> = 0.02) reducing residual heterogeneity (<emph>I</emph><sups>2</sups> = 40%, τ<sups>2</sups> = 0.021, <emph>p</emph> = 0.09). The proximal expressive vocabulary effect was large (<emph>g</emph> = 0.80), and the proximal receptive vocabulary effect was moderate (<emph>g</emph> = 0.40). The test of publication bias was also significant (<emph>p</emph> = 0.002), with two studies identified on the Egger's funnel plot (Data S2) as sources of potential publication bias (Gettinger and Stoiber [<reflink idref="bib20" id="ref81">20</reflink>]; Wilcox et al. [<reflink idref="bib72" id="ref82">72</reflink>]). A sensitivity analysis, removing these studies, substantially reduced heterogeneity (<emph>I</emph><sups>2</sups> = 22%, τ<sups>2</sups> = 0.008, <emph>p</emph> = 0.25) but also reduced the overall intervention effect (<emph>g</emph> = 0.37).</p> <hd id="AN0186282420-31">Alphabet Knowledge</hd> <p>A total of 14 studies reported alphabet knowledge outcomes with data from 3988 children (Figure 6). There were 21 effect sizes reported. The overall mean effect on alphabet knowledge was small (<emph>g</emph> = 0.23, 95% CI [0.07, 0.39], <emph>p</emph> < 0.01); however, there was significant between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 70%, τ<sups>2</sups> = 0.054, <emph>p</emph> < 0.01). The test of publication bias was not significant (<emph>p</emph> = 0.662), and there was no asymmetry on the Egger's funnel plot (Data S2). No subgroup analyses were identified, leaving a significant amount of unexplained heterogeneity.</p> <hd id="AN0186282420-32">Print Awareness</hd> <p>A total of 19 studies reported print awareness outcomes, with data from 7320 children (Figure 7). There were 28 effect sizes reported. The overall mean intervention effect on print awareness was small (<emph>g</emph> = 0.19, 95% CI [0.08, 0.31], <emph>p</emph> < 0.01); however, there was moderate to high between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 60%, τ<sups>2</sups> = 0.037, <emph>p</emph> < 0.01). The test for publication bias was not significant (<emph>p</emph> = 0.596), two studies (Huffstetter et al. [<reflink idref="bib28" id="ref83">28</reflink>]; Bailet et al. [<reflink idref="bib4" id="ref84">4</reflink>]) were identified on the Egger's funnel plot (Data S2) as possible sources of publication bias. A sensitivity analysis, excluding these studies, explained some heterogeneity (<emph>I</emph><sups>2</sups> = 43%, τ<sups>2</sups> = 0.015, <emph>p</emph> = 0.01), but also reduced the intervention effect (<emph>g</emph> = 0.14).</p> <hd id="AN0186282420-33">Phonological Awareness</hd> <p>A total of 21 studies reported phonological awareness outcomes, with data from 13,117 children (Figure 8). There were 46 effect sizes reported: 13 blending, 12 phonological awareness composite, 11 elision, five alliteration, and five first sound identification. The overall mean intervention effect on phonological awareness was small (<emph>g</emph> = 0.32, 95% CI [0.18, 0.45], <emph>p</emph> < 0.01); however, there was significant between‐study heterogeneity (<emph>I</emph><sups>2</sups> = 71%, τ<sups>2</sups> = 0.076, <emph>p</emph> < 0.01). The test for publication bias was significant (<emph>p</emph> = 0.007), with five studies (Aram [<reflink idref="bib2" id="ref85">2</reflink>]; Gettinger and Stoiber [<reflink idref="bib20" id="ref86">20</reflink>]; Griffith et al. [<reflink idref="bib22" id="ref87">22</reflink>]; Lefebvre et al. [<reflink idref="bib37" id="ref88">37</reflink>]; Wilcox et al. [<reflink idref="bib72" id="ref89">72</reflink>]) identified as possible sources of publication bias on the Egger's funnel plot (Data S2). A sensitivity analysis, excluding these studies, did not significantly reduce heterogeneity (<emph>I</emph><sups>2</sups> = 64%, τ<sups>2</sups> = 0.026, <emph>p</emph> < 0.01). Therefore, a subgroup analysis, grouping similar phonological awareness outcomes (phonological awareness composite, elision and blending, and first sound identification and alliteration) was conducted to explore this heterogeneity further. There was no statistically significant subgroup difference by phonological awareness outcome type (<emph>p</emph> = 0.11); however, the subgroup effect size differences were notable: phonological awareness composite (<emph>g</emph> = 0.08), blending and elision (<emph>g</emph> = 0.26), and first sound identification and alliteration (<emph>g</emph> = 0.24). While the phonological awareness composite subgroup had nil residual heterogeneity, overall heterogeneity remained moderate to high (<emph>I</emph><sups>2</sups> = 60%, τ<sups>2</sups> = 0.020, <emph>p</emph> < 0.01).</p> <hd id="AN0186282420-34">Synergistic Intervention Relationship</hd> <p>Evidence of a synergistic relationship between language and code outcomes was explored by conducting a meta‐regression using the composite language and code effect sizes and subgroup meta‐analyses of the composite language, composite code, and all subskill effects by intervention emphasis (Table 3).</p> <p>3 TABLE Subgroup meta‐analyses by intervention emphasis for code and language subskills and composite effects.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Intervention emphasis</th><th align="center"><italic>N</italic></th><th align="center"><italic>g</italic></th><th align="center">95% CI</th><th align="center"><italic>I</italic><sup>2</sup> (%)</th><th align="center"><italic>p</italic></th></tr></thead><tbody valign="top"><tr><td align="left">Language composite</td></tr><tr><td align="left">Code</td><td align="center">4887</td><td align="center">0.07</td><td align="center">[−0.02, 0.16]</td><td align="center">54%</td><td align="center">0.01</td></tr><tr><td align="left">Even</td><td align="center">3803</td><td align="center">0.18</td><td align="center">[0.03, 0.33]</td></tr><tr><td align="left">Language</td><td align="center">643</td><td align="center">−0.03</td><td align="center">[−0.12, 0.06]</td></tr><tr><td align="left">Code composite</td></tr><tr><td align="left">Code</td><td align="center">4860</td><td align="center">0.29</td><td align="center">[0.15, 0.42]</td><td align="center">53%</td><td align="center">0.01</td></tr><tr><td align="left">Even</td><td align="center">4314</td><td align="center">0.22</td><td align="center">[0.12, 0.33]</td></tr><tr><td align="left">Language</td><td align="center">570</td><td align="center">−0.08</td><td align="center">[−0.35, 0.19]</td></tr><tr><td align="left">Vocabulary (distal)</td></tr><tr><td align="left">Code</td><td align="center">3759</td><td align="center">0.09</td><td align="center">[0.03, 0.16]</td><td align="center">0%</td><td align="center">0.50</td></tr><tr><td align="left">Even</td><td align="center">3036</td><td align="center">0.07</td><td align="center">[−0.00, 0.15]</td></tr><tr><td align="left">Language</td><td align="center">879</td><td align="center">0.00</td><td align="center">[−0.14, 0.14]</td></tr><tr><td align="left">Vocabulary (proximal)</td></tr><tr><td align="left">Code</td><td align="center">240</td><td align="center">0.44</td><td align="center">[−0.09, 0.97]</td><td align="center">47%</td><td align="center">0.37</td></tr><tr><td align="left">Even</td><td align="center">383</td><td align="center">0.76</td><td align="center">[0.24, 1.28]</td></tr><tr><td align="left">Language</td><td align="center">260</td><td align="center">0.22</td><td align="center">[−0.50, 0.95]</td></tr><tr><td align="left">Alphabet knowledge</td></tr><tr><td align="left">Code</td><td align="center">1417</td><td align="center">0.24</td><td align="center">[0.06, 0.42]</td><td align="center">50%</td><td align="center">0.10</td></tr><tr><td align="left">Even</td><td align="center">2338</td><td align="center">0.30</td><td align="center">[0.13, 0.47]</td></tr><tr><td align="left">Language</td><td align="center">233</td><td align="center">−0.26</td><td align="center">[−0.74, 0.22]</td></tr><tr><td align="left">Phonological awareness</td></tr><tr><td align="left">Code</td><td align="center">7653</td><td align="center">0.32</td><td align="center">[0.15, 0.50]</td><td align="center">71%</td><td align="center">0.32</td></tr><tr><td align="left">Even</td><td align="center">5280</td><td align="center">0.35</td><td align="center">[0.17, 0.53]</td></tr><tr><td align="left">Language</td><td align="center">184</td><td align="center">−0.15</td><td align="center">[−0.45, 0.15]</td></tr><tr><td align="left">Print awareness</td></tr><tr><td align="left">Code</td><td align="center">3888</td><td align="center">0.22</td><td align="center">[0.07, 0.36]</td><td align="center">56%</td><td align="center">0.14</td></tr><tr><td align="left">Even</td><td align="center">3199</td><td align="center">0.22</td><td align="center">[0.08, 0.36]</td></tr><tr><td align="left">Language</td><td align="center">233</td><td align="center">−0.24</td><td align="center">[−0.69, 0.20]</td></tr></tbody></table> </ephtml> </p> <p>4 Abbreviations: CI = confidence interval; <emph>g</emph> = hedge's effect size; <emph>I</emph><sups><emph>2</emph></sups> = proportion of unexplained between‐study variance; <emph>N</emph> = number of participants.</p> <hd id="AN0186282420-35">Meta‐Regression: Composite Language and Code Outcomes</hd> <p>A positive, statistically significant relationship (<emph>p</emph> = 0.032) existed between the language composite and code composite effect sizes (Figure 9). Two outliers, Gettinger and Stoiber ([<reflink idref="bib20" id="ref90">20</reflink>]) and Landry et al. ([<reflink idref="bib34" id="ref91">34</reflink>]), were removed to reduce the potential impact of publication bias. A positive, statistically significant relationship (<emph>p</emph> = 0.028) between the language and code composite effect sizes remained.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/CDV/01jul25/cdev14252-fig-0009.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="cdev14252-fig-0009.jpg" title="9 Meta‐regression comparing language composite and code composite effect sizes (SMD, standardized mean difference)." /> </p> <p></p> <hd id="AN0186282420-37">Subgroup Meta‐Analyses: Intervention Emphasis</hd> <p>Subgroup analyses by intervention emphasis were statistically significant (<emph>p</emph> = 0.01) for the language and code composite effects (Table 3). The composite language effect was largest for interventions with an even emphasis on both code and language targets (<emph>g</emph> = 0.18). The composite code effect was largest for interventions emphasizing code targets (<emph>g</emph> = 0.29) and similarly effective for interventions with an even emphasis (<emph>g</emph> = 0.22). Language‐emphasized interventions negatively affected language (<emph>g</emph> = −0.03) and code (<emph>g</emph> = −0.08) composite effects. However, moderate between‐study heterogeneity was present. A sensitivity analysis was conducted, removing previously identified sources of publication bias. Intervention emphasis subgroup differences continued to be statistically significant for both the language (<emph>p</emph> = 0.03) and code (<emph>p</emph> < 0.01) composite effects. Between‐study heterogeneity was reduced to nil (<emph>I</emph><sups><emph>2</emph></sups> = 0%). The resulting language composite effects were slightly reduced for code (<emph>g</emph> = 0.04), even (<emph>g</emph> = 0.09), and language (<emph>g</emph> = −0.04) emphasized interventions. Similarly, the code composite effects were also slightly reduced for code (<emph>g</emph> = 0.22), even (<emph>g</emph> = 0.20), and language (<emph>g</emph> = −0.21) emphasized interventions. Overall, even and code‐emphasized interventions had the largest language and code composite effects. While subgroup meta‐analyses, at the code and language subskill level, were not statistically significant, interventions with an even emphasis were noted to have equivalent, if not larger, intervention effects than interventions with a code or language emphasis (Table 3).</p> <hd id="AN0186282420-38">Discussion</hd> <p>The aims of this systematic meta‐analytic review were to describe the characteristics of combined language and code preschool emergent literacy interventions, evaluate their effectiveness using meta‐analytic techniques, and explore any evidence of a synergistic relationship between language and code outcomes from combined intervention.</p> <hd id="AN0186282420-39">Combined Code and Language Intervention Characteristics</hd> <p>Most interventions were preschool programs delivered by preschool educators adhering to a supplemental or alternative curriculum, with the majority receiving educator training and or coaching. The high occurrence of educator training and coaching in the included interventions is noteworthy due to their known positive association with language and literacy outcomes, particularly alphabet knowledge and expressive vocabulary (Brunsek et al. [<reflink idref="bib7" id="ref92">7</reflink>]). The predominant service delivery was small group intervention but often included whole‐class activities. The majority (63%) of intervention types were explicit. They used specific session plans and a prescribed number of sessions or intervention time. There was, however, high variability in the reporting of dosage elements. Few studies reported a qualitative description of therapy techniques, and no studies reported the number of teaching episodes per session. Teaching episodes are the specific intervention moments designed to achieve a goal (Warren et al. [<reflink idref="bib68" id="ref93">68</reflink>]). Cumulative intervention time was, therefore, only calculable for 53% of interventions. Detailed dosage is particularly relevant for language interventions, as the techniques, procedures, and instruction method significantly impact intervention outcomes (Frizelle et al. [<reflink idref="bib18" id="ref94">18</reflink>]). Variable dosage reporting presents a challenge for meta‐analyses, as dosage is a significant moderator of outcomes (Frizelle et al. [<reflink idref="bib17" id="ref95">17</reflink>]). In agreement with Frizelle et al. ([<reflink idref="bib18" id="ref96">18</reflink>]), we recognize the need for a consistent taxonomy for dosage reporting in intervention studies so that future meta‐analyses can provide greater clinical translation and validity of outcomes.</p> <hd id="AN0186282420-40">Code and Language Subskill Targets</hd> <p>The full range of code subskills was well represented, including phonological awareness, alphabet knowledge, and print awareness. Contrastively, fewer language subskill targets were represented, with most interventions focusing on vocabulary, followed by comprehension and general unspecified oral language targets. While vocabulary is a critical oral language subskill known to predict future reading comprehension (Hjetland et al. [<reflink idref="bib26" id="ref97">26</reflink>]), grammar, morphology, and higher‐level language tasks such as narrative and inferencing are also significantly influential (Hjetland et al. [<reflink idref="bib26" id="ref98">26</reflink>]; Piasta et al. [<reflink idref="bib51" id="ref99">51</reflink>]; van Kleeck et al. [<reflink idref="bib66" id="ref100">66</reflink>]). Future intervention studies should include a broader range of language subskill targets.</p> <hd id="AN0186282420-41">Effectiveness of Combined Code and Language Intervention</hd> <p>Overall, combined language and code intervention had a positive but very small effect on language outcomes (<emph>g</emph> = 0.11, <emph>p</emph> < 0.01) and a small effect on code outcomes (<emph>g</emph> = 0.23, <emph>p</emph> < 0.01). At a subskill level, the code intervention effects appeared comparable with other meta‐analytic findings of domain‐specific intervention effects for at‐risk preschool children. Our phonological awareness effects of elision and blending (<emph>g =</emph> 0.26) and initial sound identification (<emph>g =</emph> 0.24) were comparable to the meta‐analytic findings of Rehfeld et al. ([<reflink idref="bib56" id="ref101">56</reflink>]); blending (<emph>g</emph> = 0.17), elision (<emph>g =</emph> 0.25), and first sound identification (<emph>g</emph> = 0.25). Similarly, our alphabet knowledge effect (<emph>g</emph> = 0.23) was equivalent to the meta‐analytic finding of Piasta and Wagner ([<reflink idref="bib53" id="ref102">53</reflink>]), specifically the combined letter knowledge and letter sound effect from domain‐specific alphabet knowledge intervention (<emph>g</emph> = 0.23).</p> <p>However, our distal vocabulary effect (<emph>g</emph> = 0.08) was significantly lower than the meta‐analytic finding of (Marulis and Neuman [<reflink idref="bib44" id="ref103">44</reflink>]; <emph>g</emph> = 0.69). A range of intervention factors may have contributed to our small language effects. Firstly, shared book reading was a central component of the language intervention in 80% of the interventions. Meta‐analytic results from Noble et al. ([<reflink idref="bib48" id="ref104">48</reflink>]) indicated that shared book reading had a very small, non‐specific effect (<emph>g</emph> = 0.14) on child language outcomes, which may have contributed to the small language intervention effects.</p> <p>Secondly, the subgroup meta‐analysis indicated a statistically significant difference between the language intervention effects of explicit interventions (<emph>g</emph> = 0.12) and distributed interventions (<emph>g</emph> = −0.01). The negative language effect from distributed interventions is clinically significant. Distributed interventions were more dispersed in language instruction and focused on enriching educational learning experiences across the preschool year. The explicit interventions, however, reported greater specificity in the intervention approach, time, and focus. As language is an unconstrained and cognitively complex interaction of subskills (Marulis and Neuman [<reflink idref="bib44" id="ref105">44</reflink>]), language intervention must also have specific subskill targets delivered with specificity and intensity. Our results demonstrated that children with low language levels from at‐risk populations need more than educational enhancement; they need specific targeted language intervention.</p> <p>Language intervention is also complex, and language gains depend on subskill target selection, dosage, and application into meaningful naturalistic discourse‐level exchanges (Frizelle et al. [<reflink idref="bib18" id="ref106">18</reflink>]; Hindman et al. [<reflink idref="bib24" id="ref107">24</reflink>]; Marulis and Neuman [<reflink idref="bib44" id="ref108">44</reflink>]). Few studies specifically targeted discourse‐level language tasks such as narrative. Including narrative intervention may have enhanced language outcomes, as it involves multiple language subskills within a complex discourse and is known to produce significant language effects (Spencer and Petersen [<reflink idref="bib63" id="ref109">63</reflink>]). Contrastively, code subskills such as alphabet knowledge and print awareness represent a finite number of concrete and tangible concepts. Interventions targeting a finite set of code subskills may be less sensitive to distributed intervention effects, which would explain why there was no statistically significant difference in code effect sizes between distributed and explicit combined interventions in our study.</p> <p>Notably, there was also a significant amount of between‐study heterogeneity in some of our meta‐analytic results. A portion of this variance was attributed to publication bias, the type of intervention, whether explicit or distributed, and the outcome measure used. However, moderate to large heterogeneity remained in alphabet knowledge, phonological awareness, and the language and code composite effects. While subgroup analyses by study quality were not significant, this heterogeneity suggests that other inappreciable methodological or intervention factors have influenced outcomes. One such factor could be that our study population combined children who were identified with speech and/or language disorder and children at risk for literacy failure based on their socioeconomic status (SES) or academic scores. Whilst there is evidence of likely overlap between these populations (Law et al. [<reflink idref="bib35" id="ref110">35</reflink>]; Smith et al. [<reflink idref="bib62" id="ref111">62</reflink>]), it is also likely that a portion of children who experience social adversity will be typically developing. These children may have a different response to combined code and language intervention than children with a diagnosed speech and/or language disorder. A subgroup analysis was not possible as studies did not report intervention effects by diagnostic group. We recommend future studies explore potential differences in response to combined code and language intervention between these groups.</p> <p>Another factor could be implementation fidelity. Several interventions, such as Literacy Express (Farver et al. [<reflink idref="bib15" id="ref112">15</reflink>]; Lonigan et al. [<reflink idref="bib41" id="ref113">41</reflink>], [<reflink idref="bib42" id="ref114">42</reflink>]), were evaluated in two or more studies within our review and were observed to produce different intervention effects. Implementation fidelity could be a contributing factor. While many studies reported elements of intervention fidelity (See Data S1), there was significant variation in the methodology applied, and it was not possible to evaluate outcomes by the dosage received. Future intervention studies would benefit from standardized fidelity reporting, not only procedural adherence to activities and tasks but also adherence to specific dosage elements.</p> <p>Additionally, consistent with current literature on language outcome measurement, language effect sizes were influenced by the assessment tool used to measure change (Marulis and Neuman [<reflink idref="bib44" id="ref115">44</reflink>]; Rogde et al. [<reflink idref="bib58" id="ref116">58</reflink>]). Proximal vocabulary effects (<emph>g</emph> = 0.53) were higher than distal (<emph>g</emph> = 0.08), and proximal expressive vocabulary effects (<emph>g</emph> = 0.80) were higher than receptive (<emph>g</emph> = 0.40). The variation in outcome measurement in our review was significant, with 43 language and 53 code assessment tools represented. This variability may have also contributed to the heterogeneity in our language and code effects. Consistent outcome measurement in future intervention studies could, therefore, enhance future meta‐analytic reviews (Frizelle et al. [<reflink idref="bib17" id="ref117">17</reflink>]).</p> <hd id="AN0186282420-42">Evidence of a Synergistic Intervention Relationship</hd> <p>Our study utilized two methods to investigate evidence of a synergistic relationship between code and language intervention effects. Firstly, we conducted a meta‐regression, which found a positive, statistically significant relationship between code and language outcomes. As language effects increased, code effects increased.</p> <p>Secondly, we conducted a subgroup meta‐analysis by intervention emphasis. We anticipated that interventions with a code emphasis would have greater code outcomes, and interventions with a language emphasis would have greater language outcomes, but what of interventions with an even emphasis on both? Our results were mixed. Interventions with a code focus had the largest effect on code outcomes and a very small effect on language. However, interventions emphasizing language had a negative effect on both language and code outcomes. Given the small number of studies (3/29) represented in the language emphasis subgroup, we are cautious in drawing conclusions from this meta‐analytic result. In particular, the subskill analyses of print awareness, alphabet knowledge, phonological awareness, and proximal vocabulary contained only one language‐emphasized study.</p> <p>The most useful comparison is between the code and even rated interventions. Even emphasized interventions had comparable code effects to the code emphasized interventions and produced the largest language effects. These results combined with the meta‐regression finding could be interpreted as evidence of a synergistic relationship between code and language intervention effects. Like the results of Lonigan et al. ([<reflink idref="bib43" id="ref118">43</reflink>]) and Gettinger and Stoiber ([<reflink idref="bib20" id="ref119">20</reflink>]), code effects were neither enhanced nor compromised by the combined intervention, producing comparable intervention effects to domain‐specific meta‐analytic intervention outcomes. Contrastively, language effects were increased by an even emphasis on code and language subskills. However, it is difficult to draw conclusions about the impact of synergistic intervention effects, given the overall small meta‐analytic outcomes. Despite the positive relationship between code and language intervention effects, outcomes were more likely impacted by other intervention characteristics such as dosage and target selection.</p> <hd id="AN0186282420-43">Limitations</hd> <p>Exploration of a synergistic relationship relied on the intervention emphasis coding, which was based on the details of the intervention reported. Most studies provided an overview of intervention targets; however, specific intervention details were highly variable. This may have influenced our intervention coding.</p> <hd id="AN0186282420-44">Conclusion and Implications for Future Research</hd> <p>This systematic meta‐analytic review found that combined language and code emergent literacy intervention for preschool children at risk of reading difficulties is effective but to a small effect. The difference between language and code outcomes was notable, with language effects much smaller than desirable. Future combined language and code intervention studies should seek to enhance oral language outcomes by explicitly targeting a greater range of language subskills, such as vocabulary, grammar, and narrative, and include discourse level activities in addition to shared book reading. Inclusion of detailed dosage and implementation fidelity characteristics would also assist in evaluating intervention effects. The statistically significant relationship between code and language outcomes, and the finding that interventions with an even emphasis on both code and language subskills resulted in the largest language and code effects, provide some evidence of a synergistic intervention relationship and the benefits of combined intervention. Future intervention research needs to explore enhancement of both language and code outcomes.</p> <hd id="AN0186282420-45">Acknowledgments</hd> <p>We are thankful to the authors who provided additional data and study context. Open access publishing facilitated by The University of Sydney, as part of the Wiley ‐ The University of Sydney agreement via the Council of Australian University Librarians.</p> <hd id="AN0186282420-46">Conflicts of Interest</hd> <p>The authors declare no conflicts of interest.</p> <hd id="AN0186282420-47">Data Availability Statement</hd> <p>The data and analytic code necessary to reproduce the analyses presented here are publicly accessible, as are the materials necessary to attempt to replicate the findings. Data are available from the first author upon reasonable request. Analyses were also pre‐registered (PROSPERO 2018 CRD42018114154). The preregistration for this research is available at the following URL: https://<ulink href="http://www.crd.york.ac.uk/prospero/display%5frecord.php?ID=CRD42018114154">www.crd.york.ac.uk/prospero/display%5frecord.php?ID=CRD42018114154</ulink>.</p> <p>GRAPH: Data S1. Search Strings</p> <p>GRAPH: Data S2. Funnel P</p> <p>GRAPH: Data S3. 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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Combined Language and Code Emergent Literacy Intervention for At-Risk Preschool Children: A Systematic Meta-Analytic Review
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jennie+Cusiter%22">Jennie Cusiter</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3033-5365">0000-0003-3033-5365</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kate+Short%22">Kate Short</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2022-0620">0000-0002-2022-0620</externalLink>)<br /><searchLink fieldCode="AR" term="%22Annabel+Webb%22">Annabel Webb</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8435-4436">0000-0001-8435-4436</externalLink>)<br /><searchLink fieldCode="AR" term="%22Natalie+Munro%22">Natalie Munro</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5870-6378">0000-0002-5870-6378</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Child+Development%22"><i>Child Development</i></searchLink>. 2025 96(4):1519-1545.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 27
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Information Analyses
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Emergent+Literacy%22">Emergent Literacy</searchLink><br /><searchLink fieldCode="DE" term="%22Meta+Analysis%22">Meta Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22At+Risk+Students%22">At Risk Students</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Language%22">Child Language</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink><br /><searchLink fieldCode="DE" term="%22Phonological+Awareness%22">Phonological Awareness</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Skills%22">Language Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/cdev.14252
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0009-3920<br />1467-8624
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This meta-analytic review explored the characteristics and effectiveness of combined language (e.g., vocabulary) and code (e.g., phonological awareness) interventions, including synergistic intervention effects for at-risk preschoolers. Data from 29 randomized controlled trials, published before March 2023, reporting on 43 interventions, including 9333 children (4-6 years; 55% male, 45% African American, 30% Hispanic) were included in the meta-analyses. Composite intervention effects were small: language (g = 0.11) and code (g = 0.23). Language and code outcomes were significantly related (p = 0.032). Interventions equally targeting code and language subskills produced equivalent or greater code and language outcomes than those with an unequal emphasis. Implications for future combined intervention studies are discussed.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1475426
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1475426
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/cdev.14252
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 27
        StartPage: 1519
    Subjects:
      – SubjectFull: Emergent Literacy
        Type: general
      – SubjectFull: Meta Analysis
        Type: general
      – SubjectFull: At Risk Students
        Type: general
      – SubjectFull: Preschool Children
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Child Language
        Type: general
      – SubjectFull: Vocabulary Development
        Type: general
      – SubjectFull: Phonological Awareness
        Type: general
      – SubjectFull: Language Skills
        Type: general
      – SubjectFull: Outcomes of Education
        Type: general
    Titles:
      – TitleFull: Combined Language and Code Emergent Literacy Intervention for At-Risk Preschool Children: A Systematic Meta-Analytic Review
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jennie Cusiter
      – PersonEntity:
          Name:
            NameFull: Kate Short
      – PersonEntity:
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            NameFull: Annabel Webb
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            NameFull: Natalie Munro
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      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0009-3920
            – Type: issn-electronic
              Value: 1467-8624
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              Value: 96
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Child Development
              Type: main
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