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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".