Thresholds of probable problematic gambling involvement for the German population: Results of the Pathological Gambling and Epidemiology (PAGE) Study.
Bibliographic record
Abstract
Consumption measures in gambling research may help to establish thresholds of low-risk gambling as 1 part of evidence-based responsible gambling strategies. The aim of this study is to replicate existing Canadian thresholds of probable low-risk gambling (Currie et al., 2006) in a representative dataset of German gambling behavior (Pathological Gambling and Epidemiology [PAGE]; N = 15,023). Receiver-operating characteristic curves applied in a training dataset (60%) extracted robust thresholds of low-risk gambling across 4 nonexclusive definitions of gambling problems (1 + to 4 + Diagnostic and Statistical Manual for Mental Disorders-Fifth Edition [DSM-5] Composite International Diagnostic Interview [CIDI] symptoms), different indicators of gambling involvement (across all game types; form-specific) and different timeframes (lifetime; last year). Logistic regressions applied in a test dataset (40%) to cross-validate the heuristics of probable low-risk gambling incorporated confounding covariates (age, gender, education, migration, and unemployment) and confirmed the strong concurrent validity of the thresholds. Moreover, it was possible to establish robust form-specific thresholds of low-risk gambling (only for gaming machines and poker). Possible implications for early detection of problem gamblers in offline or online environments are discussed. Results substantiate international knowledge about problem gambling prevention and contribute to a German discussion about empirically based guidelines of low-risk gambling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".