The Gambling Craving Scale: Psychometric validation and behavioral outcomes.
Bibliographic record
Abstract
Although craving is an important feature of problem gambling, there is a paucity of research investigating craving to gamble. A major stumbling block for craving research in gambling has been the lack of a methodologically sound, multidimensional measure of gambling-related craving. This article reports the development of the Gambling Craving Scale (GACS). In Study 1 (N = 220), a factor analysis revealed the emergence of a 9-item scale with 3 factors: Anticipation, Desire, and Relief. An important finding was that the GACS predicted problem gambling severity, depression, and positive and negative affect. In Study 2 (N = 145), the factor structure of the GACS was confirmed using a community sample of gamblers. In Study 3 (N = 46), GACS scores significantly predicted persistence at play on a virtual slot machine in the face of continued loss. Specifically, the more participants craved to gamble, the longer they engaged in play. The implications of craving for the development and maintenance of problem gambling severity are discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".