A Treatment Approach for Adolescents with Gambling Problems
Bibliographic record
Abstract
As indicated in previous chapters‚ it is not uncommon for an adolescent to be participating in one form of gambling or another‚ be it the lottery‚ card playing for money‚ sports wagering‚ or gambling on electronic gambling devices. The results of the National Research Council’s (NRC) (1999) review of empirical studies suggest that 85% of adolescents (the median of all studies) report having gambled during their lifetime‚ with 73% of adolescents (median value) reporting gambling in the past year. This raises serious mental health and public policy concerns (Derevensky‚ Gupta‚ Messerlian & Gillespie‚ in this volume; NRC‚ 1999). Meta-analyses (Shaffer & Hall‚ 1996) and a review of more recent studies (see Jacobs‚ in this volume) confirm that between 4–8% of youth are experiencing very serious gambling-related problems‚ with another 10–15% at-risk for the development of a gambling dependency. More recent debates have raised the question as to the accuracy of prevalence rates of problem gambling amongst youth. Some have recently argued that our current instruments and screens are not accurately assessing pathological gambling amongst adolescents but are over-estimating the prevalence rates (i.e‚ Ladouceur et al.‚ 2000; Jacques & Ladouceur‚ 2003). Yet‚ in a comprehensive discussion of the arguments‚ Derevensky‚ Gupta and Winters (2003) and Derevensky and Gupta (in this volume) suggest that many of the assertions raised have little merit. Nevertheless‚ while this debate plays itself out in the research community and
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".