Bibliographic record
Abstract
BACKGROUND: School-entry characteristics predict adult educational attainment, which forecasts dispositions toward disease prevention. Health and education risks can also be transmitted from one generation to the next. As such, school readiness forecasts a set of intertwined biopsychosocial trajectories that can influence the developmental antecedents to health and disease prevalence in society. PURPOSE: To predict children's health behaviors and academic adjustment at the end of fourth grade from their kindergarten entry math, vocabulary, and attention skills. METHOD: We use a subsample of 614 girls and 541 boys from the Quebec Longitudinal Study of Child Development (Canada). Children were individually assessed for cognitive skills and teachers rated their classroom attention skills at 65 months. Outcome measures include health behaviors, psychosocial, and academic outcomes at 122 months. Multiple regression analyses were used. RESULTS: Receptive vocabulary in kindergarten exclusively predicted fourth-grade dietary habits. Unstandardized coefficients predicted decreases in sweet snack intake (β = -.009, 95% confidence interval [CI] = -.011 to -.006) and dairy product intake (β = .009, 95% CI = .005 to .013). Conversely, higher kindergarten math skills predicted increases in activities requiring physical effort (β = .030, 95% CI = .011 to .056). Although vocabulary and attention skills were found important, kindergarten math skills were stronger and more consistent predictors of later academic outcomes. CONCLUSIONS: From a population-health perspective, the skills children bring to the kindergarten classroom might reduce a host of lifestyle risks from childhood through adulthood. Early promotion of such skills also offers possibilities for ultimately reducing later disparities in health and education.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".