Comparing problem gamblers with moderate-risk gamblers in a sample of university students
Bibliographic record
Abstract
BACKGROUND AND AIMS: In an effort to provide further empirical evidence of meaningful differences, this study explores, in a student population, the distinctions in gambling behavioral patterns and specific associated problems of two levels of gambling severity by comparing problem gamblers (PG) and moderate-risk gamblers (MR) as defined by the score on the Problem Gambling Severity Index (PGSI; MR: 3-7; PG: 8 and more). METHODS: The study sample included 2,139 undergraduate students (male = 800, mean age = 22.6) who completed the PGSI and questionnaires on associated problems. RESULTS: Results show that problem gamblers engage massively and more diversely in gambling activities, more often and in a greater variety of locations, than moderate-risk gamblers. In addition, important differences have been observed between moderate-risk and problem gamblers in terms of expenditures and accumulated debt. In regards to the associated problems, compared to moderate-risk gamblers, problem gamblers had an increased reported psychological distress, daily smoking, and possible alcohol dependence. DISCUSSION AND CONCLUSIONS: The severity of gambling and associated problems found in problem gamblers is significantly different from moderate-risk gamblers, when examined in a student population, to reiterate caution against the amalgamation of these groups in future research.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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 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".