Gambling motives and symptoms of problem gambling in frequent slots players
Bibliographic record
Abstract
Motives for gambling were examined among patrons of slots venues who reported playing electronic gaming machines at least weekly (N=849). According to scores on the Problem Gambling Severity Index (PGSI), there were 331 (39.0%) participants at low risk, 330 (38.9%) at moderate risk, and 188 (22.1%) at high risk of Pathological Gambling. Scores on the Coping and Enhancement scales of the Gambling Motives Questionnaire (GMQ) had independent effects on PGSI scores. Cluster analysis of Coping and Enhancement scores identified Low Emotion Regulation (LER; n=189), Primarily Enhancement (PE; n=338), and Coping and Enhancement (CE; n=322) subtypes. More CE gamblers (80.1%) had PGSI scores that suggested problem or Pathological Gambling than the PE (56.8%) or LE (36.0%) subtypes. Gamblers who frequently play slot machines are at elevated risk of Pathological Gambling if they play slots as a means of self-regulating their negative emotional states.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".