Relationship between General Health Indices and Sexual Dysfunction in Women Experiencing Spousal Abuse
Bibliographic record
Abstract
BACKGROUND: Sexual behavior is among the complex dimensions of humans. Various factors are effective in sexual function. Sexual dysfunction affects people's mental health. OBJECTIVES: The present study aims to determine the relationship between general health indices and sexual dysfunction among the women referring to Shiraz Forensic Medicine Center, Iran. METHODS: This cross-sectional, descriptive-analytical study was conducted on 197 women experiencing spousal abuse who had referred to Shiraz Forensic Medicine Center in 2014. The data were collected using demographic information questionnaire, a questionnaire assessing violence against women, general health questionnaire (GHQ-28), and female sexual function index (FSFI). The data were entered into SPSS statistical software and analyzed using t-test, Chi-square test, Pearson's correlation coefficient, and regression analysis. RESULTS: Based on the results, 91.9% of the participants had sexual dysfunction and 85.3% had mental disorders. Besides, a significant relationship was found between sexual dysfunction and somatic symptoms (P=0.027), anxiety and insomnia (P=0.004), as well as overall general health (P=0.041). Also, the results of Pearson's correlation test revealed significant associations between various dimensions of sexual dysfunction and general health. CONCLUSION: According to the results, physical and mental health seems to play a critical role in determining sexual dysfunction. Thus, the identification and treatment of physical and mental disorders are recommended for the patients suffering from sexual dysfunction.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".