The negative consequences of hypersexuality: Revisiting the factor structure of the Hypersexual Behavior Consequences Scale and its correlates in a large, non-clinical sample
Bibliographic record
Abstract
INTRODUCTION: Despite the growing literature about hypersexuality and its negative consequences, most studies have focused on the risk of sexually transmitted infections (STI's), resulting in relatively few studies about the nature and the measurement of a broader spectrum of adverse consequences. METHODS: = 11.1) and identify its factor structure across genders. The dataset was divided into three independent samples, taking into consideration gender ratio. The validity of the HBCS was investigated in relation to sexuality-related questions (e.g., frequency of pornography use) and the Hypersexual Behavior Inventory (Sample 3). RESULTS: Both the exploratory (Sample 1) and confirmatory (Sample 2) factor analyses (CFI = 0.954, TLI = 0.948, RMSEA = 0.061 [90% CI = 0.059-0.062]) suggested a first-order, four-factor structure that included work-related problems, personal problems, relationship problems, and risky behavior as a result of hypersexuality. The HBCS showed adequate reliability and demonstrated reasonable associations with the examined theoretically relevant correlates, corroborating the validity of the HBCS. CONCLUSION: Findings suggest that the HBCS may be used to assess consequences of hypersexuality. It may also be used in clinical settings to assess the severity of hypersexuality and to map potential areas of impairment, and such information may help guide therapeutic interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".