Quitting again: Motivations and strategies for terminating gambling relapses
Bibliographic record
Abstract
This study provides a descriptive exploration of the reason(s) why individuals who experienced a gambling relapse terminated the relapse episode and how they did so. Thirty-eight males and 22 females were administered the Relapse Experience Interview (Marlatt & Gordon, 1989). Participants (N = 60) cited a mean of 1.5 reasons for terminating relapse, with monetary factors, affective factors, reappraisal and external constraints emerging as central factors in relapse termination. Participants reported using a mean of 1.7 strategies for stopping a gambling episode. The strategies used were identified as either cognitive or behavioural and were classified according to the processes of change model (Prochaska, DiClemente & Norcross, 1992). Stimulus control, self-liberation, counterconditioning and helping relationships were the main strategies used to terminate gambling relapse. Participants showed a preference for using either cognitive or behavioural strategies rather than both.
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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 | 0.001 |
| 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".