Developing and Validating Lower Risk Online Gambling Thresholds with Actual Bettor Data from a Major Internet Gambling Operator
Bibliographic record
Abstract
Objective: To help individuals avoid potential negative consequences associated withtheir gambling, researchers have developed lower risk limits for time and financial involvementamong populations of land-based gamblers. The present study extended these efforts to onlinegambler populations with prospective longitudinal data. Method: We used receiver operating characteristic curve analysis and logistic regression models predicting a Brief Biosocial Gambling Screen (BBGS; Gebauer, LaBrie & Shaffer, 2010) to develop lower risk limits for six measures of gambling involvement among subscribers to an online gambling operator. We also tested the utility of these six newly-developed limits and three existing land-based limits for the BBGS outcome and proxies for gambling problems including: (1) voluntary self-limiting, (2) voluntary self-exclusion, (3) closing one's account, and (4) being assigned a flag for potential problem gambling by customer service. Results: We identified five optimal limits for lower risk online gambling with adequate sensitivity and specificity for predicting BBGS-positive status, and four of those that also predicted at least one proxy outcome in logistic regression models. These four empirically supported gambling limits were: (1) wagering 167.97 Euros or less each month; (2) spending 6.71% or less of annual income on online gambling wagers; (3) losing 26.11 Euros or less on online gambling per month; and (4) demonstrating variability (i.e., standard deviation) in daily amount wagered of 35.14 Euros or less during one's duration active. Conclusions: Our findings have implications for lower risk gambling limits research and suggest that unique limits might apply to online and land-based gambler populations.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".