Clustering of Pregnancy Wastages among Young HIV-Positive Women in a High HIV Prevalence District in India
Bibliographic record
Abstract
Context: This paper examined the clustering of pregnancy wastage among HIV-infected pregnant women, particularly focusing on the outcomes of abortions and stillbirths, from a large sample of HIV-positive women to bridge the existing information gap. Methods: Data from a cross-sectional survey conducted among currently married HIV positive women aged 15-29 years in Bagalkot district, Karnataka, India were used. Further to assess the clustering of pregnancy wastage, the binomial distribution was used. Results: The study shows that 2% of the women account for 32% of the total pregnancy wastages and 7% of all the interviewed HIV positive women account for 62% of the total pregnancy wastages. The study further shows that a higher percent observed (29.6%) than expected (15.5%) who had experienced 2 pregnancy wastages suggesting 14.1% more women experienced 2 pregnancy wastages than expected. Again, a positive deviant of 19.2 among women with at least 3 pregnancy wastage also suggests a higher observed than expected pregnancy wastages, and suggests clustering of pregnancy wastages among HIV-positive women. After adjusting for the socio-economic characteristics, the variance of the observed and expected has reduced only marginally. Conclusions: Clustering of pregnancy wastages among HIV positive women suggests for an increased attention on reproductive health of this marginalized group. The findings will be useful for the interventions working with WLHA in India, particularly the knowledge of clustering would help policy makers and program implementers to focus on high risk women who are likely to experience multiple pregnancy losses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".