Behaviors in kindergarten are associated with trajectories of long-term welfare receipt: A 30-year population-based study
Bibliographic record
Abstract
Abstract This study examines the link between behavior in kindergarten and adult-life welfare receipt. Teacher-rated behavioral assessments were obtained for inattention, hyperactivity, aggression–opposition, anxiety, and prosociality when children (n=2960) were aged 5–6 years and linked to their tax return records from age 18–35 years. We used group-based based trajectory modeling to identify distinct trajectories of welfare receipt and multinomial logistic regression models to examine the association between behaviors and trajectory group membership. The child's sex, IQ, and family background were adjusted for. Four trajectories of welfare receipt were identified: low (n = 2,390, 80.7%), declining (n = 260, 8.8%), rising (n = 150, 5.2%), and chronic (n = 160, 5.4%). Relative to the low trajectory, inattention and aggression–opposition at age 6 years were associated with increased risk of following a declining, rising, and chronic trajectory of welfare receipt, independent of hyperactivity and anxiety. Prosocial behaviors were independently associated with a lower risk of following a chronic trajectory. This study shows that kindergarten children exhibiting high inattention and aggression–opposition and low prosocial behaviors may be at increased risk of long-term welfare receipt in adulthood. The implications for early screening, monitoring, and prevention are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".