Uptake of HIV Re-Testing among Postpartum Women Who Initially Tested Negative during Antenatal Visit in Ubungo District, Dar es salaam
Bibliographic record
Abstract
BACKGROUND: It is recommended that HIV re-testing should be done at third trimester, labour and at six months postpartum. Reports indicate low rates of HIV re-testing among women after delivery. The study assessed the uptake of HIV re-testing among women during postpartum after initially testing negative at ANC clinic in Ubungo district, Dar es salaam. METHOD: A cross-sectional analytical study was conducted among women who delivered during the study period. Data was collected through a standardised questionnaire and secondary data was collected from the Health Information System and ANC cards. SPSS version 28 was used to analyse data. Bivariate and multivariate logistic regression models were used to calculate the adjusted odds ratio and to quantify the association. RESULTS: 426 women participated in the study. The overall HIV re-testing rate was 76.1%, 23.9% did not re-test and 2.1% were infected with HIV after the second test. Married women have higher re-testing rates (52.8%) than single, separated and divorcees. Women aged 25-34 years have significant higher (84.6%) proportion of re-testing for HIV than those < 25 and those > 34 years of age (p<0.001). Other significant predictors for HIV re-testing were women who made at least five or more visits to ANC and those who knew the HIV status of their partners. CONCLUSION: HIV re-testing has not reached the Tanzania national target of 95%. Efforts to sensitise communities on the importance of early ANC booking at ANC and re-testing for HIV during labour, delivery and postpartum should be enhanced.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".