Female Sexual Dysfunction in Married Medical Students
Bibliographic record
Abstract
Background: Sexuality and its manifestation constitute some of the most complex of human behavior. Sexual dysfunction is more prevalent in women than in men. Prevalence of the subgroups of female sexual disorders is: desire disorder in 5-46%, arousal disorders in 7-10% and orgasmic disorders in 7- 10%. The objective of our study was to measure the prevalence of female sexual dysfunction in female medical students. Materials and Methods: Thirty two medical students participated in the study. The mean age was 24.30± 1.29 years. Duration of marriage was 2.68±1.5 years. Their husbands’ education ranges from secondary school diploma to PhD. Persian version of Sexual Function Questionnaire (SFQ) was piloted among medical students with and without chief complaint of female sexual dysfunction. Results: Prevalence of an abnormal score in each subgroup of SFQ was as follows: 20.0% in desire, 56.7% in arousal sensation, 33.3% in arousal lubrication, 36.7% in orgasm, 6.7% in pain and 20.0% in enjoyment. In our study 40.0% had sexual problems at least in one subgroup and 6.7% had problems in all subgroups. Only 2 participants were unsatisfied with their sexual life and seeking for any treatment. Discussion: In this study, prevalence of Female Sexual Dysfunction (FSD) ranges from 6.7% to 56.7% in subgroups of the disorder. Solving social problems have critical effect on quality of life. Evaluation of FSD is important in total and especially in women who are university educated and will be occupied in essential positions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".