The Prevention of Adolescent Problem Gambling Through Probabilistic Reasoning: Evidence of the Intervention’s Efficacy
Bibliographic record
Abstract
Abstract Preventive efforts are necessary to reduce the risk for problem gambling among adolescents, especially among more at-risk youth. However, only a small proportion of the preventive initiatives implemented in the field of adolescent problem gambling are based on robust theoretical models and have been evaluated in their efficacy. By referring to the dual-process model of human functioning, especially to the mindware concept, the goal of this study was to develop and evaluate a school-based preventive intervention based on teaching probabilistic reasoning ability and explaining biases in reasoning with probability. Indeed, research with adolescents found that poor probabilistic reasoning ability is associated with gambling-related cognitive distortions that, in turn, are a risk factor for problem gambling. The study aim was to reduce gambling-related distortions by working on the concept of randomness and probability. A pre- and post-test design was performed with 72 adolescents randomly assigned to a Training group and a No Training group. Results showed a significant reduction of cognitive distortions at the post-test only in the Training group. Findings suggest that teaching probability can serve to reduce the susceptibility to gambling-related distortions and should be pointed out in the training process of the intervention providers in the gambling field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".