Predicting Gambling Behavior in Sixth Grade From Kindergarten Impulsivity
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between early impulsive behavior, rated by kindergarten teachers, and self-reported gambling in sixth grade. DESIGN: Prospective longitudinal study. SETTING: The 1999 kindergarten cohort of the Montreal Longitudinal Preschool Study in Canada. PARTICIPANTS: Written parental consent was obtained for 181 of the 377 children from intact families at kindergarten exclusively selected for follow-up telephone interviews in the fall of sixth grade, 6 years after the initial assessments. Of these, 163 children had complete data in kindergarten (mean age, 5.5 years) and sixth grade (mean age, 11.5 years) for the key variables in the analyses. Main Outcome Measure Self-reported gambling behavior in sixth grade. RESULTS: A 1-unit increase in kindergarten impulsivity corresponded to a 25% increase in later self-reported child involvement in gambling (SE = .02). This was above and beyond potential child- and family-related confounds, including parental gambling. CONCLUSIONS: Our findings offer insight about how the nature and course of early impulsivity might relate to a significantly higher propensity toward involvement in games of chance in later childhood. It is suggested that developmentally continuous risks associated with early impulsivity place individuals on a risk trajectory toward excessive gambling involvement in adolescence and emerging adulthood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".