Pilot Randomised Trial of a Brief Online Personalised Feedback Intervention for the UK Context Designed To Prevent, Reduce, and Address Gambling Harm
Bibliographic record
Abstract
Participation in online gambling is growing and the risk of experiencing harms is also increasing. Brief personalised feedback interventions have been shown to prevent, reduce and address gambling harm and this randomised controlled trial tested the effectiveness of a version customised for the UK. A sample of 1586 online gambling participants with moderate or problem gambling were rapidly recruited from an existing Internet panel of UK residents. Participants were randomised to a no intervention control group or received the self-directed, online intervention which included normative feedback and personalised information explaining the consequences of gambling above lower-risk guidelines. One- and three-month follow-ups assessed the short-term impact on frequency and harm. Feedback and recommendations were collected to guide improvements and increase future utility. All gambling outcomes showed improvement between the initial survey and both follow-ups, however, there were no differences between the intervention and control groups. Most participants displayed normative misperceptions when estimating how much others the same age and gender gambled. The majority of the sample had never previously sought treatment despite over a third of these reporting moderate or problematic levels of gambling. There is need for a publicly available, low-cost alternative to traditional treatment in order to help the large proportion of people with gambling concerns who would not otherwise seek formal support. Although an intervention effect was not detected in this sample, Internet-based alternatives remain a promising opportunity meriting further research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".