Children’s Socioemotional Strengths in Early Childhood Education (ECE) and Before/After School Care (BASC): A Multilevel Ecological Analysis
Bibliographic record
Abstract
Background/Objectives: The current study explored how trajectories of children’s socioemotional strengths were explained by school, classroom, and individual differences in the context of licensed early childhood education (ECE), involving preschool and before/after school programming. The predictive role of neighbourhood socioeconomic status (SES) was also explored. Methods: Participants included n = 226 children from 39 classrooms across seven ECE centres in a large city in Canada. Educators completed measures of children’s socioemotional strengths at three time points between January and June 2024. Children’s forward sortation areas (FSA) were also linked with publicly available data on neighbourhood SES from the 2021 census. Four-level multilevel models estimated scores across time, individual, classroom, and school levels. Results: All four levels significantly explained variance in strengths. On average, child strengths improved over the 4.5 months of ECE programming. Random slopes at the individual and classroom level revealed variability in trajectories. Higher neighbourhood SES was associated with higher socioemotional strengths and was not associated with change over time. Conclusions: The findings of this study reveal that child, classroom, school, and neighbourhood factors interact to foster child socioemotional strengths. Thus, targeted and universal programs for promoting socioemotional development in ECE must similarly adopt a multiple levels of analysis perspective.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".