II. The utility of outcome expectancies in the prediction of adolescent gambling behaviour
Bibliographic record
Abstract
The Gambling Expectancy Questionnaire (GEQ; Gillespie, Derevensky & Gupta, 2006, previous article) suggests that adolescents hold a variety of positive and negative outcome expectancies related to gambling. Significant age, gender, and DSM-IV-MR-J gambling group differences were identified on the scales of the GEQ (i.e., enjoyment/arousal, self-enhancement, money, overinvolvement, emotional impact) in this study. Direct logistic regression among adolescent gamblers was performed separately for males and females to predict group membership in either social or problem gambling categories. The results provide insightful information suggesting that non-gamblers, social gamblers, at-risk gamblers, and probable pathological gamblers (PPGs) differ in the strength of their expectancies of both the positive and negative outcomes of gambling behaviour. In particular, PPGs highly anticipate both the positive and negative outcomes of gambling. Among males, these perceptions differentiate those who gamble excessively and those who do not. For females, outcome expectancies may have less predictive value. These findings were interpreted in terms of their implications for prevention, treatment, and future research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".