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Enregistrement W4378195380 · doi:10.1371/journal.pone.0285985

Early childhood education and care quality and associations with child outcomes: A meta-analysis

2023· review· en· W4378195380 sur OpenAlexfundno aff
Antje von Suchodoletz, D. Susie Lee, Junita Henry, Supriya Tamang, Bharathy Premachandra, Hirokazu Yoshikawa

Notice bibliographique

RevuePLoS ONE · 2023
Typereview
Langueen
DomaineSocial Sciences
ThématiqueEarly Childhood Education and Development
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Mental HealthYork UniversityNew York University Abu Dhabi
Mots-clésEarly childhood educationMeta-analysisInclusion (mineral)Early childhoodGrey literatureQuality (philosophy)Child developmentMEDLINEPsychologyMedicineGerontologyDemographyDevelopmental psychologySocial psychologyPolitical science

Résumé

récupéré en direct d'OpenAlex

OBJECTIVES: The effectiveness of early childhood education and care (ECEC) programs for children's development in various domains is well documented. Adding to existing meta-analyses on associations between the quality of ECEC services and children's developmental outcomes, the present meta-analysis synthesizes the global literature on structural characteristics and indicators of process quality to test direct and moderated effects of ECEC quality on children's outcomes across a range of domains. DESIGN: A systematic review of the literature published over a 10-year period, between January 2010 and June 2020 was conducted, using the databases PsychInfo, Eric, EbscoHost, and Pubmed. In addition, a call for unpublished research or research published in the grey literature was sent out through the authors' professional network. The search yielded 8,932 articles. After removing duplicates, 4,880 unique articles were identified. To select articles for inclusion, it was determined whether studies met eligibility criteria: (1) study assessed indicators of quality in center-based ECEC programs catering to children ages 0-6 years; and (2) study assessed child outcomes. Inclusion criteria were: (1) a copy of the full article was available in English; (2) article reported effect size measure of at least one quality indicator-child outcome association; and (3) measures of ECEC quality and child outcomes were collected within the same school year. A total of 1,044 effect sizes reported from 185 articles were included. RESULTS: The averaged effects, pooled within each of the child outcomes suggest that higher levels of ECEC quality were significantly related to higher levels of academic outcomes (literacy, n = 99: 0.08, 95% C.I. 0.02, 0.13; math, n = 56: 0.07, 95% C.I. 0.03, 0.10), behavioral skills (n = 64: 0.12, 95% C.I. 0.07, 0.17), social competence (n = 58: 0.13, 95% C.I. 0.07, 0.19), and motor skills (n = 2: 0.09, 95% C.I. 0.04, 0.13), and lower levels of behavioral (n = 60: -0.12, 95% C.I. -0.19, -0.05) and social-emotional problems (n = 26: -0.09, 95% C.I. -0.15, -0.03). When a global assessment of child outcomes was reported, the association with ECEC quality was not significant (n = 13: 0.02, 95% C.I. -0.07, 0.11). Overall, effect sizes were small. When structural and process quality indicators were tested separately, structural characteristics alone did not significantly relate to child outcomes whereas associations between process quality indicators and most child outcomes were significant, albeit small. A comparison of the indicators, however, did not yield significant differences in effect sizes for most child outcomes. Results did not provide evidence for moderated associations. We also did not find evidence that ECEC quality-child outcome associations differed by ethnic minority or socioeconomic family background. CONCLUSIONS: Despite the attempt to provide a synthesis of the global literature on ECEC quality-child outcome associations, the majority of studies included samples from the U.S. In addition, studies with large samples were also predominately from the U.S. Together, the results might have been biased towards patterns prevalent in the U.S. that might not apply to other, non-U.S. ECEC contexts. The findings align with previous meta-analyses, suggesting that ECEC quality plays an important role for children's development during the early childhood years. Implications for research and ECEC policy are discussed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,356
Score d'incertitude au seuil0,930

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,283
Tête enseignante GPT0,404
Écart entre enseignants0,121 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations85
Publié2023
Routes d'admission1
Résumé présentoui

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