Online interventions for problem gamblers with and without co-occurring unhealthy alcohol use: Randomized controlled trial
Bibliographic record
Abstract
Problem gambling and unhealthy alcohol use often co-occur. The current trial sought to establish whether adding a brief online intervention for unhealthy alcohol use to an online problem gambling intervention would lead to improvements in gambling and drinking among those with both of these concerns. Participants were recruited from across Canada using an advertisement targeting those concerned about their gambling who were interested in online help. No mention of unhealthy alcohol use was made in the advertisement. Participants meeting criteria for problem gambling were randomized to either receive just an online intervention for gambling (G-only) or to receive an online gambling intervention plus a brief personalized feedback intervention for unhealthy alcohol use (G + A). Participants were followed up at 3 and 6 months. A total of 282 participants were recruited for the trial. Follow-up rates were good (80% and 84% at 3 and 6 months). There were significant reductions in gambling (p < .001) across time but no significant differences (p > .05) between those who received either the G-only or G + A interventions. Further, for those with unhealthy alcohol use (41% of the sample), there were no significant reductions in alcohol consumption (p > .05) across time or differences between condition. The addition of a brief intervention for unhealthy alcohol use to an online intervention for gambling did not appear to improve either gambling or drinking outcomes among people concerned about their gambling. Further research is merited to examine whether a combined intervention (with gambling and drinking components integrated) might result in improved outcomes and whether such an intervention might benefit the subgroup of participants who would specifically seek help for both gambling and alcohol concerns. Trial registration: ClinicalTrials.gov NCT03323606; Registration date: October 24, 2017.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".