Assessing the effect of COVida orphans and vulnerable children support services on viral load coverage and suppression among children and adolescents living with HIV in four provinces in Mozambique
Bibliographic record
Abstract
Orphans and vulnerable children (OVC) programs focusing on improving HIV outcomes for children and adolescents living with HIV (C&ALHIV) may improve viral load (VL) testing coverage, a critical step toward achieving VL suppression. In Mozambique, we conducted a retrospective medical record review comparing VL testing coverage and suppression between C&ALHIV receiving OVC support and two cohorts of non-participants constructed using propensity score matching. We collected data for 25,783 C&ALHIV in Inhambane, Maputo City, Nampula, and Tete between October 2020-September 2021. Unadjusted rates of VL testing were 62.9% among OVC participants compared with 39.2% and 50.4% of non-participants in OVC support and non-OVC support districts, respectively. In multivariate models, OVC participants were 18 and 10 percentage points more likely to have received a VL test than non-participants in OVC districts (p < 0.01) and non-OVC districts (p < 0.01), respectively. OVC participants under 5 years old were significantly more likely to have received a VL test than their same-age counterparts in both comparison groups. Overall, the OVC program did not demonstrate significant effects on VL suppression. This approach could be replicated in other contexts to improve testing coverage. It is crucial that clinical partners and governments continue to share data to enable timely monitoring through OVC programming.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".