Reaching out to big losers leads to sustained reductions in gambling over 1 year: a randomized controlled trial of brief motivational contact
Bibliographic record
Abstract
BACKGROUND AND AIMS: A previous randomized controlled trial demonstrated that telephone- and letter-based motivational interventions with high-expenditure gamblers had significant short-term positive effects on gambling and use of responsible gambling tools. This post-trial follow-up examined outcomes in gambling expenditure over 12 months. DESIGN: Observational study following a three-arm randomized controlled trial. SETTING: Customers of Norsk Tipping (NT) gambling platforms, Norway. PARTICIPANTS: A total of 1003 statistical triplets from the top 0.5% of customers based upon annual expenditure, matched on sex, age and net losses. Mean age was 53.4 years; 19% were women, mean yearly loss for 2016 was 88 197 NoK. Interventions and comparator Feedback intervention by telephone, letter or a no-contact control condition. MEASUREMENTS: Primary outcome measure was gambling theoretical loss, derived from the NT customer database. Secondary outcomes were responsible gambling customer actions and whether or not the participant was retained as an NT customer. FINDINGS: Per-protocol analyses of triplets who received the telephone call or letter as randomly assigned (n = 596) showed a positive and sustained effect over 12 months: the telephone group showed a 30% reduction in theoretical loss (d = 0.44) and the letter group 13% (d = 0.18), both outperforming the control group with a 7% reduction (d = 0.11). The telephone condition was superior to both the letter and control conditions in per-protocol (P < 0.001) and to control condition in intention-to-treat analyses (ITT) (P < 0.001). Individuals in the telephone condition took more responsible gambling actions. The letter condition had better outcomes than the control in the ITT-only analysis (P < 0.001). More than 93% were still customers a year after the intervention. CONCLUSIONS: Personal contact with high-expenditure gambling customers in Norway that provided individualized feedback on expenditures was associated with reduced theoretical losses and greater use of responsible gambling tools over a 12-month period, compared with no contact. Telephone intervention with customers had a larger impact than a mailed letter.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".