A practical clinical guide to counselling on and managing contraception, pre-conception planning, and menopause for women living with HIV
Bibliographic record
Abstract
Background: Women represent one quarter of the population living with HIV in Canada and are an increasingly important sector of the HIV community. While some women's health issues such as cervical cancer screening and management are well addressed in HIV management guidelines, others are not. These include sexual and reproductive health factors such as contraception, pre-conception planning, and menopause. Existing literature has shown that while women living with HIV in Canada receive good HIV care based on HIV care cascade indicators, their women's health and sexual and reproductive health care needs are not being met. Methods: In this article, we present a clinical guide for clinicians providing care for women living with HIV on three key women's health topics that are under-discussed during HIV care visits: (1) contraception, (2) pre-conception planning, and (3) menopause. Results: We have summarized the most pertinent clinical factors on each topic to support straightforward counselling and present important considerations in the context of HIV-related diseases and treatment. Finally, when relevant, we have provided practical stepwise approaches for addressing each of these women's health care topics when seeing a patient during a visit. Conclusions: It is important that HIV specialists stay well-versed in the complex clinical interactions between HIV treatment and management of women's health issues.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.030 |
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".