The Gambling Craving Scale: Psychometric validation and behavioral outcomes.
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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