Life events and problem gambling severity: A prospective study of adult gamblers.
Bibliographic record
Abstract
Several studies have shown that gambling problems are cyclical but few have empirically investigated factors that are associated with change. The purpose of this article is to prospectively examine associations between life events and problem gambling severity in a cohort of gamblers. Occurrence of life events and gambling problem severity were assessed 3 times over a period of 2 years in a cohort of nonproblem and problem gamblers (N = 179) drawn from a representative sample derived from a population survey. Cross-lagged analyses revealed that cumulative number of life events were associated with an increase in severity of problem gambling 12 months later. Regression analyses showed that significant life events in several domains, for example, "change in sleeping habits," "accidental injury or illness" or "retirement," are likely to be associated over time to the increase or the continuation of risky gambling habits. This study's findings on the potential negative influence of cumulative number of life events, or of specific ones, are informative for secondary prevention and treatment. (PsycINFO Database Record
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".