Prevalence and correlates of female sexual dysfunction and sexual distress in reproductive-aged women: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Female sexual dysfunction (FSD) is defined as any dissatisfaction with sexual function domains such as sexual desire/arousal disorder, orgasmic disorders, and genital-pelvic pain/penetration disorder that in some cases leads to sexual distress (SD). OBJECTIVE: This systematic review and meta-analysis aimed to examine the prevalence and associated factors of FSD and SD among healthy reproductive-aged women. METHODS: In this systematic review and meta-analysis, Google Scholar and electronic databases such as Scopus, ScienceDirect, PubMed, Web of Science, and the Iranian database including the Scientific Information Database (SID), were searched and the publication year of the included articles were limited to January 1, 2015, through January 1, 2024 to identify studies that assessed FSD and SD among reproductive-aged women. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale for cross-sectional and cohort studies. RESULTS: Twenty studies were included in this review. According to the results of 18 studies, the prevalence of FSD ranged between 20.6% and 95.0%. In addition, the prevalence of sexual desire disorder (SDD) was estimated between 8.0% and 91.0% based on the results of 16 studies. The prevalence of arousal disorder (9.0-91.0%), orgasm disorder (7.9-93.0%), lubrication problems (9.3-99.0%), pain disorder (8.2-99.0%), and satisfaction (21.4-86.0%). SD prevalence was 31.8-83.4%. Meta-analysis revealed a pooled FSD prevalence of 47.81% (95% CI: 39.19-56.43%). The factors related to FSD were classified into five main categories: sociodemographic, reproduction, interpersonal, psychological, and medical factors. Also, the associated factors of SD were classified into four categories: sociodemographic, interpersonal, psychological, and medical factors. The risk of bias for all of the included studies was good. CONCLUSION: The results of this study showed that reproductive-aged women are at high risk of FSD and SD due to various factors. As the literature regarding SD among the reproductive-aged population was limited, conducting high-quality cross-sectional studies with representative samples and using validated questionnaires are required to provide more precise data regarding its prevalence and associated factors across diverse cultural contexts. TRIAL REGISTRATION: PROSPERO; Registration no. CRD42024491942.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".