Pregnancy wastage among HIV infected women in a high HIV prevalence district of India
Bibliographic record
Abstract
BACKGROUND: Bagalkot district in Karnataka state is one of the highest HIV prevalence districts in India. A large proportion of the girls also marry at early age in the district and negative pregnancy outcomes among the HIV positive women likely to have large pregnancy wastages. Therefore, this study examined the pregnancy wastages and the associated factors among HIV positive women in a high prevalent district in India. METHODS: We used data from a cross-sectional survey conducted recently among randomly selected currently married HIV positive women, 15-29 years of age, in one of the high HIV prevalence districts in India. The study used the experience of reported pregnancy wastage as an outcome variable, and both bi-variate and multivariate logistic regression analyses were carried out to understand the factors associated with the pregnancy wastage among HIV infected women. RESULTS: Overall, 17% of the respondents reported pregnancy wastage, of which 81% were due to spontaneous abortions. Respondents who became pregnant since testing HIV positive reported significantly higher level of pregnancy wastage as compared to those were pregnant before they were tested for HIV. (AOR = 1.9; p = 0.00). While a positive association between duration of marriage and pregnancy wastage was noticed (AOR = 7.4; p = 0.01), there was a negative association between number of living children and pregnancy wastage (AOR = 0.24; p = 0.00). Living in a joint family was associated with increased reporting of pregnancy wastage as compared to those living in nuclear families (AOR = 1.7; p = 0.03). CONCLUSIONS: HIV prevention and care programs need to consider the reproductive health needs of HIV infected married women as a priority area since large proportion of these women reported negative pregnancy outcomes. There is also a need to explore ways to raise the age at marriage in order to stop women getting married before the legal age at marriage.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".