Co-occurring compulsive sexual behaviour in an inpatient substance use population: Clinical correlates and influence on treatment outcomes
Bibliographic record
Abstract
Background and Aims: Many individuals with substance use disorders (SUDs) present with co-occurring mental health disorders and other addictions, including behavioral addictions (BAs). Though several studies have investigated the relationship between SUDs and BAs, less research has focused specifically on compulsive sexual behaviour (CSB). Given that poly-addiction can hinder treatment outcomes, it is necessary to better understand the impact of co-occurring CSB and SUD. Therefore, the current study aimed to 1) determine the rate of CSB in a sample seeking treatment for SUDs, 2) identify demographic and clinical correlates of co-occurring CSB, and 3) to determine if co-occurring CSB impacts treatment outcomes for SUD. Methods: Participants were 793 adults (71.1% men) ranging in age from 18-77 (M = 38.73) at an inpatient treatment facility for SUDs who were assessed for CSB upon admission into treatment. Participants completed a battery of questionnaires upon admission and at discharge to assess psychological and addiction symptoms. Results: Rates of CSB were 24%. Younger age and being single were associated with greater CSB. Mental distress and addiction symptoms were higher in participants with CSB. Predictors of CSB severity included greater symptoms of traumatic stress and interpersonal dysfunction. Rates of treatment completion were similar between participants with and without CSB. Discussion and Conclusions: These results highlight several clinical and demographic correlates of CSB amongst individuals in treatment for SUD. However, CSB was not associated with poorer treatment outcomes. Further identifying characteristics associated with CSB can help clinicians identify individuals who may be at higher risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".