A Treatment-Oriented Typology of Self-Identified Hypersexuality Referrals
Bibliographic record
Abstract
Men and women have been seeking professional assistance to help control hypersexual urges and behaviors since the nineteenth century. Despite that the literature emphasizes that cases of hypersexuality are highly diverse with regard to clinical presentation and comorbid features, the major models for understanding and treating hypersexuality employ a "one size fits all" approach. That is, rather than identify which problematic behaviors might respond best to which interventions, existing approaches presume or assert without evidence that all cases of hypersexuality (however termed or defined) represent the same underlying problem and merit the same approach to intervention. The present article instead provides a typology of hypersexuality referrals that links individual clinical profiles or symptom clusters to individual treatment suggestions. Case vignettes are provided to illustrate the most common profiles of hypersexuality referral that presented to a large, hospital-based sexual behaviors clinic, including: (1) Paraphilic Hypersexuality, (2) Avoidant Masturbation, (3) Chronic Adultery, (4) Sexual Guilt, (5) the Designated Patient, and (6) better accounted for as a symptom of another condition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".