Development and validation of the gambling problems scale
Bibliographic record
Abstract
The purpose of the present study was to examine the initial psychometric properties of the Gambling Problems Scale (GPS), developed for the college student population. Participants were college students recruited for an ongoing larger clinical trial from a Midwestern university who reported gambling in the past 60 days and who were experiencing gambling-related problems, scoring +3 on the South Oaks Gambling Screen or +1 on the Brief Biopsychosocial Gambling Index (N = 334). Factor analyses and reliability analyses were conducted to examine the validity of score interpretation and the reliability of scores for the measure. Results suggested a 16-item unidimensional measure provided the best parsimony and theoretical fit. Examination of concurrent and incremental validity of scores provided additional support for the psychometric properties of the GPS. The GPS may be a useful tool for researchers and clinicians interested in examining gambling-related problems among college students and other young adults.
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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 | 0.001 | 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 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".