Probing gambling urge as a state construct: Evidence from a sample of community gamblers.
Bibliographic record
Abstract
Little effort has been made to systematically test the psychometric properties of the Gambling Craving Scale (GACS; Young & Wohl, 2009). The GACS is adapted from the Questionnaire on Smoking Urges (Tiffany & Drobes, 1991) and thus measures gambling-related urge. Crucially, the validation of scales assessing gambling urge is complex because this construct is better conceptualized as a state (a transient and context-determined phenomenon). In the present study, we tested the psychometric properties of the French version of the GACS with 2 independent samples of community gamblers following an induction procedure delivered through an audio-guided imagery sequence aimed at promoting gambling urge. This procedure was specifically used to ensure the assessment of gambling urge as a state variable. Participants also completed measures of gambling severity, gambling cognitions and motives, impulsivity, and affect. Confirmatory factor analysis showed that the original 3-factor solution (anticipation, desire, relief) did not fit the data well. Additional exploratory factor analysis suggested instead a 2-factor solution: an intention and desire to gamble dimension and a relief dimension. The factorial structure resulting from the exploratory factor analysis was tested with confirmatory factor analysis in a second independent sample, resulting in an acceptable fit. The 2 dimensions presented good internal reliability and correlated differentially with the other study's variables. The current study showed that, similar to what has been reported for substance-related urges, gambling urges are adequately probed with a bidimensional model. The findings suggest that the French GACS has good psychometric properties, legitimizing its use in research and clinical practice. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".