GamblingLess: A Randomised Trial Comparing Guided and Unguided Internet-Based Gambling Interventions
Bibliographic record
Abstract
There is little evidence relating to the effects of adding guidance to internet-based gambling interventions. The primary aim was to compare the effectiveness of an online self-directed cognitive-behavioural gambling program (GamblingLess) with and without therapist-delivered guidance. It was hypothesised that, compared to the unguided intervention, the guided intervention would result in superior improvements in gambling symptom severity, urges, frequency, expenditure, psychological distress, quality of life and help-seeking. A two-arm, parallel-group, randomised trial with pragmatic features and three post-baseline evaluations (8 weeks, 12 weeks, 24 months) was conducted with 206 gamblers (106 unguided; 101 guided). Participants in both conditions reported significant improvements in gambling symptom severity, urges, frequency, expenditure, and psychological distress across the evaluation period, even after using intention-to-treat analyses and controlling for other low- and high-intensity help-seeking, as well as clinically significant changes in gambling symptom severity (69% recovered/improved). The guided intervention resulted in additional improvements to urges and frequency, within-group change in quality of life, and somewhat higher rates of clinically significant change (77% cf. 61%). These findings, which support the delivery of this intervention, suggest that guidance may offer some advantages but further research is required to establish when and for whom human support adds value.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".