Factors at Play in Faster Progression for Female Pathological Gamblers
Bibliographic record
Abstract
BACKGROUND: Previous studies reported a faster progression for alcohol dependence and pathological gambling among females as compared with males. This phenomenon was called the "telescoping effect." By comparing female gamblers with male gamblers regarding gambling preferences and comorbidity, the authors explored potential risk factors for telescoping. METHOD: A consecutive sample of Brazilian treatment-seeking pathological gamblers (DSM-IV criteria) was recruited. Genders were contrasted regarding comorbidity and gambling behavior, controlling for demographics, gambling severity, and previous access to mental health services. RESULTS: Seventy female gamblers and 70 male gamblers were interviewed. A greater proportion of women than men reported electronic bingo and video lottery terminals as their main type of gambling. Gambling was divided in 3 progressive stages: "social gambling," "intense gambling," and "problem gambling." Faster progression for female gamblers was confirmed; female gender and preference for electronic bingo and/or video lottery terminals were risk factors for telescoping throughout all stages. Female gamblers presented a higher comorbidity with depression, whereas male gamblers had higher rates of alcohol dependence. Nevertheless, comorbidity profiles were not related to gambling progression. CONCLUSION: Two factors could be at play for treatment-seeking female gamblers in Brazil: (1) a potential gender vulnerability and (2) a cultural environment yielding them access to a narrower range of gambling games that includes mainly the most addictive ones.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".