In search of lower risk gambling levels using behavioral data from a gambling monopolist
Bibliographic record
Abstract
Background and aims: Lower-risk recommendations for avoiding gambling harm have been developed as a primary prevention measure, using self-reported prevalence survey data. The aim of this study was to conduct similar analyses using gambling company player data. Methods: The sample (N = 35,753) were Norsk Tipping website customers. Gambling indicators were frequency, expenditure, duration, number of gambling formats and wager. Harm indicators (financial. social, emotional, harms in two or more areas) were derived from the GamTest self-assessment instrument. Receiver operating characteristics (ROC) curves were performed separately for each of the five gambling indicators for each of the four harm indicators. Results: ROC areas under the curve were between 0.55 and 0.68. Suggested monthly lower-risk limits were less than 8.7 days, expenditure less than 54 €, duration less than 72-83 min, number of gambling formats less than 3 and wager less than 118-140€. Most risk curves showed a rather stable harm level up to a certain point, from which the increase in harm was fairly linear. Discussion: The suggested lower-risk limits in the present study are higher than limits based on prevalence studies. There was a significant number of gamblers (5-10%) experiencing harm at gambling levels well below the suggested cut-offs and the risk increase at certain consumption levels. Conclusions: Risk of harm occurs at all levels of gambling involvement within the specific gambling commercial environment assessed in an increasingly available gambling market where most people gamble in multiple commercial environments, minimizing harm is important for all customers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".