Brief telephone interventions for problem gambling: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: Problem gambling is a significant public health issue world-wide. There is substantial investment in publicly funded intervention services, but limited evaluation of effectiveness. This study investigated three brief telephone interventions to determine whether they were more effective than standard helpline treatment in helping people to reduce gambling. DESIGN: Randomized clinical trial. SETTING: National gambling helpline in New Zealand. PARTICIPANTS: A total of 462 adults with problem gambling. INTERVENTIONS AND COMPARATOR: (1) Single motivational interview (MI), (2) single motivational interview plus cognitive-behavioural self-help workbook (MI + W) and (3) single motivational interview plus workbook plus four booster follow-up telephone interviews (MI + W + B). Comparator was helpline standard care [treatment as usual (TAU)]. Blinded follow-up was at 3, 6 and 12 months. MEASUREMENTS: Primary outcomes were days gambled, dollars lost per day and treatment goal success. FINDINGS: There were no differences across treatment arms, although participants showed large reductions in gambling during the 12-month follow-up period [mean reduction of 5.5 days, confidence interval (CI) = 4.8, 6.2; NZ$38 lost ($32, $44; 80.6%), improved (77.2%, 84.0%)]. Subgroup analysis revealed improved days gambled and dollars lost for MI + W + B over MI or MI + W for a goal of reduction of gambling (versus quitting) and improvement in dollars lost by ethnicity, gambling severity and psychological distress (all P < 0.01). MI + W + B was associated with greater treatment goal success for higher gambling severity than TAU or MI at 12 months and also better for those with higher psychological distress and lower self-efficacy to MI (all P < 0.01). TAU and MI were found to be equivalent in terms of dollars lost. CONCLUSIONS: In treatment of problem gambling in New Zealand, brief telephone interventions are associated with changes in days gambling and dollars lost similar to more intensive interventions, suggesting that more treatment is not necessarily better than less. Some client subgroups, in particular those with greater problem severity and greater distress, achieve better outcomes when they receive more intensive treatment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".