Impulsivity and socio‐economic status interact to increase the risk of gambling onset among youth
Bibliographic record
Abstract
AIMS: To determine if impulsivity and socio-economic status (SES) interact to influence gambling onset in youth. DESIGN: Longitudinal study of grade 7 students followed for 8 years. SETTING: Montréal, Canada. PARTICIPANTS: A total of 628 adult students aged 12.6 years on average at cohort inception. MEASUREMENTS: Impulsivity and SES (parent education, area deprivation) were collected during secondary school. Age of gambling onset was collected retrospectively when participants were aged 20.3 years. Cox proportional hazards regression was used to model the association between time to first report of gambling and interaction terms for each of impulsivity and parent education, and impulsivity and area deprivation accounting for sex and ethnicity. FINDINGS: Median (interquartile range) age of gambling onset was 17.0 (4.0) years. Impulsivity independently increased the risk of gambling onset among participants with no university-educated parent [hazard ratio (HR) 1.3; 95% confidence interval 1.1-1.5] and those living in highly deprived areas (HR 1.7; 1.5-2.0). Impulsivity was not associated with gambling onset among high SES youth. Among participants with high impulsivity, risks were elevated for those with no university-educated parent relative to one or more university-educated parent (HR 1.7; 1.1-2.7), and for participants living in deprived relative to advantaged areas (HR 5.0; 2.6-9.6). SES was not associated with gambling onset among participants with low impulsivity. CONCLUSIONS: Impulsivity is a risk factor for gambling onset among low but not high SES youth, and low SES influences gambling onset primarily among impulsive youth. Gambling prevention programmes may need to consider potential interaction between impulsivity and SES.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".