LGBQ-affirming clinical recommendations for compulsive sexual behavior disorder
Bibliographic record
Abstract
Background and aims: Since the inclusion of Compulsive Sexual Behavior Disorder (CSBD) in the International Classification of Diseases (11th ed.), there has been little effort placed into developing clinical recommendations for lesbian, gay, bisexual, and queer (LGBQ) clients with this condition. Thus, we develop preliminary clinical recommendations for mental health professionals working with LGBQ clients who may be struggling with CSBD. Methods: The present paper synthesizes the CSBD literature with advances in LGBQ-affirming care to develop assessment and treatment recommendations. These recommendations are discussed within the context of minority stress theory, which provides an empirically supported explanation for how anti-LGBQ stigma may contribute to the development of mental health conditions in LGBQ populations. Results: Assessment recommendations are designed to assist mental health professionals in distinguishing aspects of an LGBQ client's sociocultural context from CSBD symptomology, given recent concerns that these constructs may be wrongly conflated and result in misdiagnosis. The treatment recommendations consist of broadly applicable, evidence-based principles that can be leveraged by mental health professionals of various theoretical orientations to provide LGBQ-affirming treatment for CSBD. Discussion and Conclusions: The present article provides theoretically and empirically supported recommendations for mental health professionals who want to provide LGBQ-affirming care for CSBD. Given the preliminary nature of these recommendations, future research is needed to investigate their clinical applicability and efficacy.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".