Correlates of gambling among youth in an inner-city emergency department.
Bibliographic record
Abstract
Correlates of past year gambling were examined in a diverse sample of 1128 youth ages 14 to 18 (54.1% female, 58.0% African American) presenting to an inner-city emergency department (ED). Overall, 22.5% of the sample reported past-year gambling. Male youth were more likely to gamble than female youth, and African American youth reported higher rates of past-year gambling than non-African American youth. Significant bivariate correlates of gambling included lower academic achievement, being out of school, working more than 20 hours per week, alcohol, cigarette, and marijuana use, alcohol problems, severe dating violence, moderate and severe general violence, and carrying a weapon. When examined simultaneously, being male, African American, out of school, working for pay, alcohol and marijuana use, severe general violence, and carrying a weapon all emerged as significant correlates of past-year gambling, largest amount of money gambled, and gambling frequency. In addition, involvement in severe dating violence was associated with frequency and largest amount gambled. The results suggest that gambling is common among youth in the inner city and is associated with several risk behaviors. The inner-city ED may provide a context for screening and intervention to address multiple risk behaviors. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".