Lost Opportunities to Reduce Periconception HIV Transmission
Bibliographic record
Abstract
INTRODUCTION: Safer conception strategies create opportunities for HIV-serodiscordant couples to realize fertility goals and minimize periconception HIV transmission. Patient-provider communication about fertility goals is the first step in safer conception counseling. METHODS: We explored provider practices of assessing fertility intentions among HIV-infected men and women, attitudes toward people living with HIV (PLWH) having children, and knowledge and provision of safer conception advice. We conducted in-depth interviews (9 counselors, 15 nurses, 5 doctors) and focus group discussions (6 counselors, 7 professional nurses) in eThekwini District, South Africa. Data were translated, transcribed, and analyzed using content analysis with NVivo10 software. RESULTS: Among 42 participants, median age was 41 (range, 28-60) years, 93% (39) were women, and median years worked in the clinic was 7 (range, 1-27). Some providers assessed women's, not men's, plans for having children at antiretroviral therapy initiation, to avoid fetal exposure to efavirenz. When conducted, reproductive counseling included CD4 cell count and HIV viral load assessment, advising mutual HIV status disclosure, and referral to another provider. Barriers to safer conception counseling included provider assumptions of HIV seroconcordance, low knowledge of safer conception strategies, personal feelings toward PLWH having children, and challenges to tailoring safer sex messages. CONCLUSIONS: Providers need information about HIV serodiscordance and safer conception strategies to move beyond discussing only perinatal transmission and maternal health for PLWH who choose to conceive. Safer conception counseling may be more feasible if the message is distilled to delaying conception attempts until the infected partner is on antiretroviral therapy. Designated and motivated nurse providers may be required to provide comprehensive safer conception counseling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".