Natural course of gambling disorders: Forty-month follow-up
Bibliographic record
Abstract
The natural course of gambling disorders was examined in 40 active pathological gamblers following a three-and-a-half-year period. The majority who reported intentions to quit or reduce gambling made a serious change attempt; however, at follow-up most were gambling problematically. Emotional and financial factors were important precipitants of attempts to quit as well as reasons for relapse. A substantial number experienced a depressive episode or substance use disorder during the follow-up period. A number reported quitting drinking and smoking concurrent with quitting gambling. Less than half had treatment for their gambling problem during the follow-up interval. The few participants who were currently gambling but no longer experiencing gambling problems reported less serious gambling problems initially. In contrast, the successfully abstinent group reported more gambling problems initially. This study provides important directions for future research. Abstinence may be more feasible for individuals experiencing more serious problems, whereas non-abstinent goals may be realistic for individuals with fewer negative consequences.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".