Integrating HIV and Maternal, Neonatal and Child Health Services in Rural Malawi: An Evaluation of the Implementation Processes and Challenges
Bibliographic record
Abstract
INTRODUCTION: Introducing Option B+ in Malawi increased antiretroviral therapy coverage among pregnant and breastfeeding women 3 fold. The Promoting Retention among Infants and Mothers Effectively (PRIME) study integrated care of Maternal, Neonatal and Child Health services through a mother-infant pair (MIP) clinic. This article evaluates the implementation processes and challenges health care workers' experienced in implementing these MIP clinics. METHODS: Between May 2013 and August 2016, 20 health facilities implemented MIP clinics. Health care workers' performance implementing MIP clinics was assessed through a mentorship score from 0 to 5 and supplemented with qualitative data from mentorship reports. Visit alignment of participants' appointment and attendance dates with MIP clinic dates were calculated and summarized by overall proportions among all patient visits. RESULTS: The average mentorship score was 3.8, improving from 3.0 to 4.2 from quarter one 2015 to quarter one 2016. Proportions of maternal and infant appointment dates that aligned with MIP clinic dates were 47.0% and 55.9%, with greatest improvement between 2013 and 2015. Proportions of maternal and infant attendance dates that aligned with MIP clinic dates were 41.7% and 51.2% and improved over time. DISCUSSION: Despite improvement in staff mentorship scores, many MIPs were not exposed to integrated HIV and Maternal, Neonatal and Child Health services offered through MIP clinics primarily because of clinic scheduling challenges. To improve utilization of integrated MIP clinics, careful design of a delivery approach is needed that is acceptable to clinic staff, addresses local realities, and includes appropriate investment and oversight.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".