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Record W2996364705 · doi:10.5206/eei.v29i3.9385

A Study of the Long-Term Influence of Early Childhood Education and Care on the Risk for Developing Special Educational Needs

2019· article· en· W2996364705 on OpenAlexvenueno aff
Edward Melhuish, Jacqueline Barnes, J. STANLEY GARDINER, Iram Siraj, Pam Sammons, Κathy Sylva, Brenda Taggart

Bibliographic record

VenueExceptionality Education International · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhood educationPsychologyNumeracyEarly childhoodDevelopmental psychologyCognitive developmentCognitionLiteracyPedagogy

Abstract

fetched live from OpenAlex

Specialized preschool programs can enhance the development of vulnerable young children at risk of special educational needs (SEN). Less is known about the potential of early childhood education and care (ECEC) provided for the general population. This study includes 2,857 children attending 141 ECEC centres in England and 310 with no ECEC. ECEC quality and effectiveness were assessed. Children’s scores on assessments of cognitive development, numeracy, and literacy, and teacher reports of socio-emotional problems at ages 5, 7, 11, and 16 years were used to identify risk of SEN (1 standard deviation beyond the mean). Trend analyses (none vs. low, medium, and high ECEC quality or effectiveness) examined impact of ECEC on risk for cognitive or socio-emotional SEN. Better quality and more effective ECEC reduced risk of cognitive SEN at 5, 11, and 16 years of age, with similar results for socio-emotional SEN. The discussion considers the consistency of the association between children’s ECEC experience and risk for SEN, which is found for alternative measures of ECEC, quality derived from observations and effectiveness derived from progress in child outcomes. These different sources for the ECEC measures add credibility to the results. Also the implications for policy and practice are discussed including the recommendation for universal provision of high quality ECEC and ensuring that the most at-risk populations receive the best ECEC available.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.326
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2019
Admission routes1
Has abstractyes

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