Gender differences in psychiatric comorbidity and personality characteristics among adults seeking treatment for problematic internet use
Bibliographic record
Abstract
In the present study, we investigated gender differences in personality and psychiatric correlates among adults (N = 115) seeking treatment for problematic internet use (PIU) at a specialized clinic in São Paulo, Brazil. All participants were assessed at the beginning of their treatment for co-occurring psychiatric conditions, other addictive behaviors, and personality characteristics. Women (n = 20) were more likely to present with greater rates of psychiatric comorbidity compared to men (n = 95), including mood disorders, anxiety disorders, obsessive-compulsive disorder, post-traumatic stress disorder, and bulimia nervosa. Women also had a greater severity of certain behavioral addictions, such as compulsive buying and disordered eating. Gender differences were also found across personality characteristics, with women scoring higher on impulsivity, novelty seeking, and self-transcendence compared to men. To our knowledge, the present study is the first to investigate gender differences for PIU in a clinical sample. The results suggest that there are notable gender differences in individuals seeking treatment for PIU which underscores the importance of assessing for co-occurring conditions, especially in women. Understanding the characteristics associated with PIU can help serve to inform the most appropriate interventions to bolster treatment outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".