A Treatment Approach for Adolescents with Gambling Problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it