Effectiveness of a web-based self-help tool to reduce problem gambling: A randomized controlled trial
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Bibliographic record
Abstract
Background and Aims: Problem gambling constitutes a public health concern associated with psychopathological comorbidity, substance use, and financial difficulties. Most individuals with gambling problems avoid counseling services due to perceived stigma and their preference for self-reliance. Treatment accessibility could be improved through web-based interventions. Methods: We recruited 360 individuals with gambling problems and randomized them to a web-based intervention (n = 185) or an active control group consisting of a self-help manual for problem gambling (n = 175). The primary outcome was the number of days of gambling in the last 30 days. Secondary outcomes included money spent in the last 30 days, time gambling in the last 7 days, gambling-related problems, consumption of alcohol and cigarettes, and psychopathological comorbidity measured at posttreatment and 6-month follow-up. Results: The primary outcome decreased significantly for both groups, with no significant difference between the groups. There were significant group × time interactions according to the Gambling Symptom Assessment Scale (F = 8.83, p <0 .001), the Problem Gambling Severity Index (F = 3.54, p = 0.030), for cigarettes smoked in the last 7 days (F = 26.68, p < 0.001), the Patient Health Questionnaire-9 (F = 19.41, p <0 .001), and the Generalized Anxiety Disorder-7 (F = 41.09, p <0 .001) favoring the intervention group. We experienced an overall high dropout rate (76%). Conclusions: Win Back Control seems to be an effective low-threshold treatment option for individuals with gambling problems that might otherwise be unapproachable for outpatient treatment services. Nevertheless, the high dropout rate should be considered when interpreting the study results, as they may have introduced a degree of variability.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it