Gender differences in psychiatric comorbidity and treatment-seeking among gamblers in treatment
Bibliographic record
Abstract
Objectives: To assess the effects of gender on comorbid problems and treatment-seeking among gamblers in treatment and the effects of comorbid problems on participants' gambling Method: Participants completed a survey on comorbid problems and the effects of comorbid problems on their gambling Sample: Seventy-eight adults (40 males, 38 females) enrolled in state-supported outpatient programs or Gamblers Anonymous Results: The majority of participants (53%) had multiple comorbid problems and 38.5% said they had a comorbid problem related to their gambling. Eleven different types of comorbid problems were reported. Females had significantly more comorbid problems than males; females reported problem drinking and both genders reported that depression increased the severity of their gambling problems. Conclusion: Patterns of comorbid problems and treatment-seeking are consistent with well-known gender differences in health behaviors. Clinicians involved in gambling treatment may wish to assess for depressive syndromes and problem drinking and investigate their interaction with their patient's gambling.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".