Gender Differences in Compulsive Buying Disorder: Assessment of Demographic and Psychiatric Co-Morbidities
Bibliographic record
Abstract
Compulsive buying is a common disorder found worldwide. Although recent research has shed light into the prevalence, etiology and clinical correlates of compulsive buying disorder, less is known about gender differences. To address this empirical gap, we assessed potential gender differences in demographic and psychiatric co-morbidities in a sample of 171 compulsive buyers (20 men and 151 women) voluntarily seeking treatment in São Paulo, Brazil. A structured clinical interview confirmed the diagnosis of compulsive buying. Of the 171 participants, 95.9% (n = 164) met criteria for at least one co-morbid psychiatric disorder. The results found that male and female compulsive buyers did not differ in problem severity as assessed by the Compulsive Buying Scale. However, several significant demographic and psychiatric differences were found in a multivariate binary logistic regression. Specifically, male compulsive buyers were more likely to report being non-heterosexual, and reported fewer years of formal education. In regards to psychiatric co-morbidities, male compulsive buyers were more likely to be diagnosed with sexual addiction, and intermittent explosive disorder. Conversely, men had lower scores on the shopping subscale of the Shorter PROMIS Questionnaire. The results suggest that male compulsive buyers are more likely to present with co-morbid psychiatric disorders. Treatment planning for compulsive buying disorder would do well to take gender into account to address for potential psychiatric co-morbidities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".