Alexithymia, Emotional Instability, and Vulnerability to Stress Proneness in Patients Seeking Help for Hypersexual Behavior
Bibliographic record
Abstract
This article reports the findings of a study investigating alexithymia, emotional instability, and vulnerability to stress proneness among individuals (N = 120) seeking help for hypersexual behavior. At the onset of treatment at an outpatient community clinic, subjects completed the Sexual Compulsivity Scale (SCS), the 20-item Toronto Alexithymia Scale (TAS-20), and the NEO Personality Inventory Revised (NEO-PI-R). The results of a hierarchical regression analysis revealed the best model in predicting severity of hypersexual behavior included the facets of depression and vulnerability to stress from the NEO and the Difficulty Identifying Feelings (DIF) factor of the TAS-20. Although the NEO domain of neuroticism appeared to capture the majority of variance in hypersexual behavior, the difficulty identifying feelings factor of the TAS-20 did make some modest, but significant, contribution to the severity of hypersexual behavior after controlling for depression and vulnerability to stress. These data provide evidence for the hypothesis that individuals who manifest symptoms of hypersexual behavior are more likely to experience deficits in affect regulation and negative affect (including alexithymia, depression, and vulnerability to stress). Possible reasons for these results are suggested and future recommendations for research are offered.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".