Gynecological Care Among Brazilian Women Who Have Sex with Women: A Respondent-Driven Sampling Study
Bibliographic record
Abstract
Purpose:This study aimed to describe the gynecological care provided to Brazilian women who have sex with women (WSW). Methods:Respondent-driven sampling was used to recruit Brazilian WSW. The survey questions, concerning gynecological care, were designed in Portuguese by medical professionals, medical students, and LGBTQIA+ community members, including the authors. The statistical analyses were weighted to account for the likelihood of recruitment. Results:From January to August of 2018, 299 participants were recruited in 14 recruitment waves. The mean age of the WSW was 25.3 years. Most (54.9%) identified as lesbian and had been involved in past-year sexual intercourse mainly with cisgender women (86.1%). The WSW also reported having sex with cisgender men (22.2%), transgender men (5.3%), nonbinary people (2.3%), and transgender women (5.3%) in the last year. More than a quarter of the WSW did not have regular appointments with a gynecologist: 8.0% (95% confidence interval [CI] = 4.2–11.6) and 19% (95% CI = 12.8–25.2) stated that they had never gone to the gynecologist or they had only gone for emergencies, respectively. Almost one-third had never had cervical cancer screening (cervical cytology, Pap test or Pap smear). Most women justified avoiding the test because they felt healthy, thought it would hurt, or feared a health professional might mistreat them. Conclusion:Gynecologists should avoid heteronormative assumptions, inquire about sexual practices, orientation, and identity separately, and provide Pap tests as advised to WSW.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".