Reaching out to big losers: A randomized controlled trial of brief motivational contact providing gambling expenditure feedback.
Bibliographic record
Abstract
Gambling disorder is a public health issue in many countries, and expectations that the gambling industry protects individuals from harm are increasing. The primary objective of this study was to investigate the effects of providing personalized feedback on gambling intensity among high consumers of venue-based and online gambling in Norway. A randomized controlled trial design was used to evaluate how behavioral feedback by telephone or letters sent via surface mail affects subsequent gambling expenditure and use of responsible gambling tools and whether a follow-up contact increases the effect. Gambling expenditure, the primary outcome, was measured using theoretical loss, which is the actual cost to the player, adjusted for the house advantage. From the top .5% of customers based upon annual expenditure, a sample of 1,003 statistical triplets, matched on sex, age, and net losses, were randomly assigned to the feedback intervention by telephone, letter, or a no-contact control condition. Participants assigned to the phone call or letter were also randomly assigned to receive or not receive a subsequent follow-up contact. The results showed that over 12 weeks, theoretical loss decreased 29% for the phone and 15% for the letter conditions, compared with 3% for the control group. A positive effect of the follow-up contact was limited to participants who at the initial call indicated an interest in receiving a follow-up call. Contacting high consumers about their gambling expenditure appears to be an effective method for gambling companies to meet their duty to care for customers. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.000 |
| 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) | 0.011 | 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".