Profiles of Low School Readiness Among At-Risk Preschoolers and Their Correlates in Kindergarten and Grade 1
Bibliographic record
Abstract
School readiness is a key early-life factor enabling children’s well-being and school adjustment across their life course. School readiness is a multidimensional concept that includes socioemotional (e.g., social skills, behaviors) and cognitive (e.g., pre-academic, language) skills. In the past decade, school readiness has been increasingly studied with a person-centered approach. This approach identifies subgroups or profiles of children with similar patterns of strengths and weaknesses in multiple indicators of school readiness. In this study, a latent profile analysis was conducted to identify profiles of school readiness among 300 French-Canadian preschoolers who are at risk of low school readiness. We also determined how they differed on their social and academic adjustment in kindergarten and Grade 1. Four profiles of school readiness were identified: Language Strength (20%), Ready for School (20%), Generalized Difficulties (40%), and Low Cognitive (21%) skills. These profiles significantly differed in their social adjustment, cognitive learning skills, and academic achievement in mathematics and in language arts. The Low Cognitive profile generally presents more difficulties than other profiles on all outcomes. This study highlights the strengths and vulnerabilities of preschoolers that should be targeted in early interventions, depending on the child’s specific profile of school readiness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".