Pathways to Kindergarten: A Latent Class Analysis of Children’s Time in Early Education and Care
Bibliographic record
Abstract
Research Findings Using a sample of 568 students from 61 kindergarten classrooms whose primary caregivers completed a questionnaire describing their child’s early childhood education and care (ECEC) by year from birth to pre-kindergarten, we identified seven pathways characterizing children’s ECEC experiences using a latent class analysis. Once identified, profile membership was included as an independent variable in a multilevel model to predict children’s cognitive and social-behavioral outcomes at kindergarten entry. Although a considerable body of work has examined dosage of time in (ECEC) and its associations with children’s skills in later grades, we extend this work by expanding the definition of dosage to include multiple care arrangements from birth to kindergarten entry and by examining if profiles of ECEC participation have associations with kindergarten-entry skills. Our findings show membership in profiles in which children spent consistent time in center-based care from birth to five were associated with adverse social-behavioral outcomes including behavioral aggression, school adjustment, peer social skills, and self-efficacy. Practice or Policy: Our findings suggest the importance of considering more nuanced differences in children’s experiences with ECEC and the need for possible interventions to support the social-behavioral development of children with exposure to 5 years of center-based care.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".