Stability and Progression of Disordered Gambling: Lessons from Longitudinal Studies
Bibliographic record
Abstract
OBJECTIVE: Few studies have explicitly examined the stability (that is, the tendency for individuals to stay at one diagnostic level as opposed to moving to another improved or worsened level) or progression of disordered gambling; however, conventional wisdom holds that disordered gambling is intractable and escalating. The objective of this study was to examine these assumptions. METHOD: We reviewed 5 recent prospective studies of gambling behaviour among nontreatment samples for evidence related to the stability and progression of disordered gambling. RESULTS: Our review found no evidence to support the assumption that individuals cannot recover from disordered gambling (that is, the persistence assumption), no evidence to support the assumption that individuals who have more severe gambling problems are less likely to improve than individuals who have less severe gambling problems (that is, the selective-stability assumption), and no evidence to support the assumption that individuals who have some gambling problems are more likely to worsen than individuals who do not have gambling problems (that is, the progression assumption). CONCLUSION: Contrary to professional and conventional wisdom suggesting that gambling problems are always progressive and enduring, this review demonstrates instability and multidirectional courses in disordered gambling.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".