Factors associated with loss-to-follow-up of HIV-positive mothers and their infants enrolled in HIV care clinic: A qualitative study
Bibliographic record
Abstract
BACKGROUND: In Malawi, loss to follow-up (LTFU) of HIV-positive pregnant and postpartum women on Option B+ regimen greatly contributes to sub-optimal retention, estimated to be 74% at 12 months postpartum. This threatens Malawi's efforts to eliminate mother-to-child transmission of HIV. We investigated factors associated with LTFU among Mother-Infant Pairs. METHODS: We conducted a qualitative study, nested within the "Promoting Retention Among Infants and Mothers Effectively (PRIME)" study, a 3-arm cluster randomized trial assessing the effectiveness of strategies for improving retention of mother-infant pairs in HIV care in Salima and Mangochi districts, Malawi. From July to December 2016, we traced and interviewed 19 LTFU women. In addition, we interviewed 30 healthcare workers from health facilities where the LTFU women were receiving care. Recorded interviews were transcribed, translated and then analysed using deductive content analysis. RESULTS: The following reasons were reported as contributing to LTFU: lack of support from husbands or family members; long distance to health facilities; poverty; community-level stigma; ART side effects; perceived good health after taking ART and adoption of other alternative HIV treatment options. CONCLUSION: Our study has found multiple factors at personal, family, community and health system levels, which contribute to poor retention of mother-infant pairs in HIV care.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".