Development and psychometric evaluation of a three‐dimensional Gambling Motives Questionnaire
Bibliographic record
Abstract
AIMS: This study was designed to develop and evaluate a self-report measure of gambling motives. Participants A community-recruited sample of 193 gamblers (70% male; mean age = 35.5 years) were selected to fill two groups according to scores on the South Oaks Gambling Screen: probable pathological gamblers (PPG; n = 154) and non-pathological gamblers (NPG; n = 39). MEASURES: Participants completed a novel 15-item measure of gambling motives called the Gambling Motives Questionnaire (GMQ), which was modeled after the original Drinking Motives Questionnaire, as well as a variety of gambling behavior and problem criterion measures. RESULTS: An exploratory principal components analysis revealed three intercorrelated factors tapping enhancement (ENH), coping (COP), and social (SOC) motives, respectively. Each GMQ subscale showed good internal consistency (alphas > 0.80). The PPG group scored higher on all three scales than the NPG group, with larger differences for ENH and COP. In line with the clinical literature, PPG women scored higher than PPG men on the COP subscale but also, unexpectedly, on the SOC subscale. In concurrent validity analyses, ENH consistently predicted greater gambling behavior, and COP and ENH consistently predicted more severe gambling problems. With gambling behavior levels controlled, only COP remained a significant predictor of gambling problem severity. Finally, gender interacted with gambling motives in predicting gambling problem severity: COP predicted gambling problems more strongly in women, whereas ENH predicted gambling problems more strongly in men. CONCLUSIONS: The GMQ appears to be a promising tool for both research and clinical applications with problem gamblers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".