Gender Differences in Gambling Disorder: Results from an Italian Multicentric Study.
Bibliographic record
Abstract
Objective: Although gender-specific evidence on Gambling Disorder (GD) is still limited, some studies reported specific differences, mainly in psychopathological profiles, gambling behavior patterns, and pathogenesis. In order to further examine the role of gender in GD, we conducted a multicenter investigation in a sample of Italian outpatients. Method: One hundred-four outpatients with a diagnosis of GD based on DSM-5 criteria were consecutively recruited at two clinics based in Milan. Socio-demographic and clinical variables were collected for the whole sample and analyzed for the effect of gender. The severity of illness was assessed using the Canadian Problem Gambling Index and the Gambling Attitudes and Beliefs (GABS). Results: Among females, a significantly higher mean age (52.23 ± 10.95 vs. 40.96 ± 15.76; p=0.005) and older age at illness onset emerged (43.5 ± 11.92 vs. 29.22 ± 14.26; p<0.001). Females showed a significantly higher rate of psychiatric comorbidities, lifetime suicide ideation, stressful events at GD onset, and positive family history for GD compared to males. A predictive effect of male gender was found for the GABS questionnaires by performing a linear regression model, with males showing a higher risk to reach higher scores compared to females (B= 11.833; t=2.177; p=0.034). Conclusions: Our study seems to confirm the hypotheses that gender in GD may influence psychopathological profiles, course, and comorbidity. GD in female gender is frequently a comorbid condition with other specific clinical characteristics compared to males. Identifying specific clinical factors by gender may prompt more focus on the public health of women in relation to gambling, while still recognizing that males are at-risk of earlier gambling problems. These findings should be considered in therapeutic perspectives.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".