Women Who Report Having Sex With Women: British National Probability Data on Prevalence, Sexual Behaviors, and Health Outcomes
Bibliographic record
Abstract
OBJECTIVES: We estimated the prevalence of same-sex experience among women and compared women reporting sex with women and men and women reporting sex exclusively with women with women reporting sex exclusively with men, in terms of sociodemographics and sexual, reproductive, and general health risk behaviors and outcomes. METHODS: We used a British probability survey (n=6399 women, aged 16 to 44 years) conducted from 1999 to 2001 with face-to-face interviewing and computer-assisted self-interviewing. RESULTS: We found that 4.9% of the women reported same-sex partner(s) ever; 2.8% reported sex with women in the past 5 years (n=178); 85.0% of these women also reported male partner(s) in this time. Compared with women who reported sex exclusively with men, women who reported sex with women and men reported significantly greater male partner numbers, unsafe sex, smoking, alcohol consumption, and intravenous drug use and had an increased likelihood of induced abortion and sexually transmitted infection diagnoses (age-adjusted odds ratios=3.07 and 4.41, respectively). CONCLUSIONS: For women, a history of sex with women may be a marker for increased risk of adverse sexual, reproductive, and general health outcomes compared with women who reported sex exclusively with men. A nonjudgmental review of female patients' sexual history should help practitioners discuss risks that women may face.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.001 |
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".