The Impact of Structured Mentor Mother Programs on Presentation for Early Infant Diagnosis Testing in Rural North-Central Nigeria: A Prospective Paired Cohort Study
Bibliographic record
Abstract
BACKGROUND: Early infant diagnosis (EID) by 2 months of age is an important prevention of mother-to-child cascade step that serves as an early postpartum indicator of program success. Uptake and timely presentation for infant HIV diagnosis are significant challenges in resource-limited settings. Few studies on maternal peer support (PS) have demonstrated impact on EID. The MoMent study evaluated the impact of structured PS on timely presentation for EID testing in rural North-Central Nigeria. METHODS: A total of 497 HIV-positive pregnant women were consecutively recruited at 10 primary health care centers with structured, closely supervised Mentor Mother (MM) support, and 10 pair-matched primary health care centers with routine but ad hoc PS. EID was assessed among HIV-exposed infants delivered to recruited women, and was defined by presentation for DNA polymerase chain reaction testing between 35 and 62 days of life. A logistic regression model with generalized estimating equation to account for clustering was used to assess the effect of MMs on EID presentation. RESULTS: Data from 408 live-born infants were available for analysis. Exposure to MM support was associated with higher odds of timely EID presentation among infants, compared with routine PS (adjusted odds ratios = 3.7, 95% confidence interval: 2.8 to 5.0). CONCLUSIONS: Closely supervised, organized MM support significantly improved presentation for EID among HIV-exposed infants in a rural Nigerian setting. Structured PS can improve rates of timely EID presentation and potentially the uptake of EID testing in resource-limited settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".