Effectiveness of a voluntary casino self-exclusion online self-management program
Bibliographic record
Abstract
Despite evidence for effectiveness, only a small proportion of individuals with gambling disorder ever access treatment and support resources for their problem. Voluntary self-exclusion (VSE) programs are an ideal circumstance to engage individuals who are reluctant or have not yet sought formal treatment, given that individuals are already electing to prevent themselves from gambling through self-exclusion. The present study was a randomized controlled trial of a novel, online VSE self-management intervention. Individuals who chose to self-exclude at gambling venues (N = 201) were randomly assigned to participate in an online self-management program combined with VSE or to an in-person self-awareness educational workshop combined with VSE comparison group. Following a baseline assessment, participants were followed up at three, six, and twelve months via telephone interviews. Measured outcomes were gambling frequency and expenditure, problem gambling scores, problem drinking scores, type of goal set for gambling behaviour, quality of life, and treatment-seeking. The 12-month follow-up rate was 71% (n = 143). Participants in both VSE groups gambled less, spent less money gambling, and reported decreased need for formal treatment. However, there were no significant group differences on any of the primary or secondary outcomes. Only 30–35% of the participants completed their assigned workshop, depending on the group. Results from the online program satisfaction survey revealed that participants generally liked the program and rated the quality of the content highly, but thought there could be improvement regarding interactivity, variety, stimulation and greater clarity around registration steps and program objectives. The online VSE program is an effective alternative to the face-to-face VSE program. Although the outcomes between the two programs were not significantly different, the online program is easier to administer, able to reach more individuals since it only requires access to a computer and is based on motivational evidence-based principles of psychotherapy for gambling disorder.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".