Defining Male Support During and After Pregnancy From the Perspective of HIV‐Positive and HIV‐Negative Women in Durban, South Africa
Bibliographic record
Abstract
INTRODUCTION: Greater male support during pregnancy and in the postpartum period may improve health outcomes for mothers and children. To develop effective strategies to engage men, we need to first understand the ways that men are currently engaged and the barriers to their greater involvement. METHODS: We conducted in-depth interviews in isiZulu with 30 HIV-positive women and 16 HIV-negative women who received prenatal care from a public clinic in Durban, South Africa. Interviews were audiotaped, transcribed, translated, and coded for analysis. RESULTS: Although less than a quarter of women reported that their partners accompanied them to the clinic, they described receiving other material and psychosocial support from partners. More HIV-positive women reported that their partners were not involved or not supportive, and in some cases direct threats and experiences with violence caused them to fear partner involvement. DISCUSSION: We need to broaden the lens through which we consider male support during pregnancy and in the postpartum period and acknowledge that male involvement may not always be in the best interest of women. Engaging supportive partners outside of the clinic setting and incorporating other important social network members are important next steps in the effort to increase support for women.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".