Can Temperament Predict School Readiness in At-Risk Kindergarteners? A Combination of Variable-Oriented and Person-Oriented Approaches
Bibliographic record
Abstract
Research Findings: In this study, a combination of variable-oriented and person-oriented statistical analyses was used to examine the links between three temperament factors (negative affectivity, surgency/extraversion, effortful control) evaluated before entry into kindergarten and the cognitive and socioemotional dimensions of school readiness measured at the end of kindergarten. The sample included 98 children considered to be at risk because of their poor school readiness seven months before kindergarten entry. Multiple linear regressions showed that the temperament factors were associated differentially with the school readiness dimensions at the end of kindergarten. Three school readiness profiles (moderate cognitive and socioemotional risk, high socioemotional risk, high cognitive risk) were identified through latent profile analyses. A multinomial logistic regression showed that the temperament factors helped predict membership in the profiles. Practice or policy: Temperament thus represents an important determinant of school readiness and could be used to identify, within an at-risk population, children who are likely to present risks of a different nature at the end of kindergarten. Prevention programs and closer supervision during the transition to school could then be offered to these children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".