Why Problem Gamblers Quit Gambling: A Comparison of Methods and Samples
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
Reasons for quitting gambling were reported by two samples of problem gamblers, long-term quitters and recent quitters, using two methodologies. The participants first described their reasons for quitting in an open-ended fashion and then with a 15-item checklist. There was a fair degree of similarity between the responses of the two samples. Both groups most frequently reported that their resolution was a conscious decision, although recent quitters were more likely to report that the decision evolved over a long period of time vs. being an immediate decision. About a third of both groups described their decision as related to a specific event and involving a crisis. In both methods, negative emotions and financial concerns were most often reported. The checklist method yielded about three times as many reasons as the open-ended method. There were no gender differences in reasons although participants with a treatment history reported more reasons than those who had not sought treatment.
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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 | 0.003 | 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) | 0.005 | 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