Identifying indicators of harmful and problem gambling in a Canadian sample through receiver operating characteristic analysis.
Bibliographic record
Abstract
Many gamblers would prefer to reduce gambling on their own rather than to adopt an abstinence approach within the context of a gambling treatment program. Yet responsible gambling guidelines lack quantifiable markers to guide gamblers in wagering safely. To address these issues, the current investigation implemented receiver operating characteristic (ROC) analysis to identify behavioral indicators of harmful and problem gambling. Gambling involvement was assessed in 503 participants (275 psychiatric outpatients and 228 community gamblers) with the Canadian Problem Gambling Index. Overall gambling frequency, duration, and expenditure were able to distinguish harmful and problematic gambling at a moderate level. Indicators of harmful gambling were generated for engagement in specific gambling activities: frequency of tickets and casino; duration of bingo, casino, and investments; and expenditures on bingo, casino, sports betting, games of skill, and investments. Indicators of problem gambling were similarly produced for frequency of tickets and casino, and expenditures on bingo, casino, games of skill, and investments. Logistic regression analyses revealed that overall gambling frequency uniquely predicted the presence of harmful and problem gambling. Furthermore, frequency indicators for tickets and casino uniquely predicted the presence of both harmful and problem gambling. Together, these findings contribute to the development of an empirically based method enabling the minimization of harmful or problem gambling through self-control rather than abstinence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".