Development of the temptations for gambling questionnaire: A measure of temptation in recently quit gamblers
Bibliographic record
Abstract
An important factor in understanding relapse in problem gambling is the temptation to gamble. This article evaluates the factor structure and the psychometric properties of the Temptations for Gambling Questionnaire (TGQ), a new measure of temptation to gamble in 21 high-risk situations. The TGQ was administered to 101 recently quit pathological gamblers (65 males and 36 females). Principal components analysis supported a four-factor structure, with factors representing Negative Affect, Positive Mood/Impulsivity, Seeking Wins or Money, and Social Factors. Construct validity of the scale was supported by the consistency of the factors with social learning theory, Marlatt's cognitive behavioural model of relapse, and prior research on gambling relapse. Internal consistency of the TGQ and its factors was strong (α = 0.80–0.91). The TGQ holds promise as a reliable and valid measure of temptation to gamble.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".