Why don't adolescents turn up for gambling treatment (revisited)?
Bibliographic record
Abstract
In a previous issue of the Electronic Journal of Gambling Issues, Griffiths (2001) raised 10 speculative reasons as to why so few adolescents enrol for treatment programs when compared with adults. This paper explores the issue a little further with another 11 possible reasons. These are (i) adolescents don't seek treatment in general; (ii) adolescents may seek other forms of treatment, but gambling problems are less likely to be seen as requiring intervention; (iii) treating other underlying problems may help adolescent gambling problems; (iv) a dolescent gambling 'bail-outs' can mask gambling problems; (v) a ttending treatment programs may be stigmatising for adolescents; (vi) adolescents may commit suicide before getting treatment; (vii) a dolescent gamblers may be lying or distorting the truth when they fill out survey questionnaires; (viii) a dolescents may not understand what they are asked in questionnaires; (ix) screening instruments for adolescent problem gambling are being used incorrectly; (x) adolescent gambling may be socially constructed to be nonproblematic; and (xi) adolescent excesses may change too quickly to warrant treatment.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".