Comparing problem gamblers with moderate-risk gamblers in a sample of university students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it