Assessment methods and management of hypersexuality and paraphilic disorders
Bibliographic record
Abstract
PURPOSE OF REVIEW: The recent implementation of the Diagnostic and Statistical Manual of Mental Disorders, fifth edition introduced some important changes in the conceptualization of hypersexuality and paraphilic disorders. The destigmatization of nonnormative sexual behaviors could be viewed as positive, However, other changes are more controversial. In order to stimulate new research approaches and provide mental healthcare providers with appropriate treatment regimes, validated assessment and treatment methods are needed. The purpose of this article is to review the studies published between January 2013 and July 2014 that aimed at assessing the psychometric properties of the currently applied assessment instruments and treatment approaches for hypersexuality and hypersexual disorders or paraphilias and paraphilic disorder. RECENT FINDINGS: Currently existing instruments can validly assess hypersexual behaviors in different populations (e.g. college students, gay and bisexual men, and patients with neurodegenerative disorders) and cultural backgrounds (e.g. Germany, Spain, and USA). Concerning the assessment of paraphilias, it was shown that combining different assessment methods show a better performance in distinguishing between patients with paraphilias and control groups. In addition to psychotherapeutic treatment, pharmacological agents aiming at a reduction of serum testosterone levels are used for hypersexual behaviors as well as paraphilic disorders. SUMMARY: Although the currently applied assessment and treatment methods seem to perform quite well, more research about the assessment and evidence-based treatment is needed. This would help to overcome the existing unresolved issues concerning the conceptualization of hypersexual and paraphilic disorders.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".