Kindergarten Readiness, Later Health, and Social Costs
Bibliographic record
Abstract
OBJECTIVES: To estimate associations between kindergarten readiness and academic, psychological, and health risks by end of high school. METHODS: This study is based on 966 Canadian children. At age 5, trained examiners assessed child number knowledge and receptive vocabulary. Teachers reported kindergarten classroom engagement. At age 17, participants reported academic grades, school connectedness, anxiety sensitivity, substance abuse, physical activity involvement, and height and weight. High school dropout risk was also estimated for each participant on the basis of grades, school engagement, and grade retention. RESULTS: Kindergarten math skills contributed to better end-of high school grades (β = .17, P < .01) and lower dropout risk (β = −.20, P < .001), whereas receptive vocabulary predicted lower anxiety sensitivity (β = −.11, P < .01). Kindergarten classroom engagement predicted higher end of high school grades (β = .17, P < .001), lower dropout risk (β = −.20, P < .01), better school connectedness (β = .15, P < .01), lower risk of substance abuse (β = −.21, P < .001), and more physical activity involvement (β = .09, P < .05). Kindergarten classroom engagement was also associated with a 65% reduction (odds ratio = 0.35) in the odds of being overweight at age of 17. Analyses were adjusted for key child (sex, weight per gestational age, nonverbal IQ, and internalizing and externalizing behaviors) and family (parental involvement, maternal depression and immigrant status, family configuration, and socioeconomic status) characteristics. CONCLUSIONS: Early childhood readiness forecasts a protective edge by emerging adulthood. With these findings, we build links between education and health indicators, suggesting that children who start school prepared gain a lifestyle advantage. Promoting kindergarten readiness could reduce the health burden generated by high school dropout.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.001 |
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".