School Readiness and Early Childhood Education and Care Services Among Dual Language Learners
Bibliographic record
Abstract
Importance: Dual language learners (DLL) (ie, children learning 2 or more languages) present lower school readiness than non-DLL children, putting DLL children at risk of later school difficulties and adverse outcomes. However, it is unclear whether participation in early childhood education and care (ECEC) services may reduce this gap. Objective: To assess whether ECEC exposure may reduce the school readiness gap between DLL and non-DLL children in a population-based sample. Design, Setting, and Participants: This census survey study was performed from February to May 2022 in the Canadian province of Quebec using data from the Quebec Survey of Child Development in Kindergarten, which includes all children who attended kindergarten in the 2021 to 2022 school year in public and private schools in Quebec (n = 80 587), except for Cree and Inuit territories. Exposure: Children's ECEC arrangement before kindergarten was retrieved from register-based data and teachers and arrangements were categorized as exclusive parental care, childcare, pre-kindergarten only, or childcare and pre-kindergarten. Based on their mother tongue and language of instruction, children were classified as French speaking, English speaking, bilingual French-English speaking, or neither French nor English speaking (allophone) children, the last 2 groups of which represented the DLL category. Main Outcomes and Measures: Vulnerability in school readiness was defined as a score below the 10th percentile in any of the 5 domains of the validated Early Development Instrument (EDI): (1) physical health and well-being; (2) social competence; (3) emotional maturity; (4) language and cognitive development; and (5) communication skills and general knowledge. Results: In total, 80 587 children were surveyed, and 71 585 children were included in analyses. Mean (SD) child age was 6.0 (0.3) years, 34 911 children (48.8%) were female, and 18 341 children (25.6%) were DLL. English-speaking, bilingual French-English-speaking, and allophone children were more likely to be vulnerable in the EDI (769 of 2355 children [32.7%], 4814 of 13 981 children [34.4%], and 1622 of 4360 children [37.2%], respectively) than French-speaking children (13 664 of 50 890 children [26.9%]). In logistic regression analyses adjusted for social selection bias in ECEC arrangement, attending ECEC services was associated with a lower risk of being vulnerable among all language groups compared to parental care, with odds ratios ranging from 0.26 (95% CI, 0.25-0.27) to 0.96 (95% CI, 0.80-1.14), except in the emotional maturity domain. ECEC exposure was associated with reduction in vulnerabilities disparities between DLL and non-DLL children after adjusting for confounding factors, including socioeconomic status. Conclusions and Relevance: ECEC services may foster school readiness for all children, especially DLL, and should be considered to reduce school inequalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".