Zero-Dose Identification and Reach Using Electronic Community Health Records in Mozambique: A Type 2 Hybrid Effectiveness-Implementation Study Protocol
Bibliographic record
Abstract
Background/Objectives Despite global progress, about 24% of children in Mozambique remain zero-dose. Geographic, socioeconomic, and systemic barriers hinder vaccine access, and caregivers often face long travel distances, stockouts, and poor service experiences. The Zero-Dose Identification and Reach initiative seeks to strengthen the Expanded Programme on Immunisation by enhancing the upSCALE digital health platform. This implementation research will evaluate the effectiveness, feasibility, and acceptability of the enhanced immunization module in identifying and vaccinating zero-dose and under-immunized children, while generating evidence for national scale-up. Methods This type 2 hybrid effectiveness-implementation study will be conducted in two districts of Zambezia Province. A non-randomized controlled trial will compare intervention (Mocuba) and control (Nicoadala) districts through baseline and endline household surveys of care-givers of children aged 3–59 months (n=440 per arm). Primary outcomes are the pro-portion of zero-dose and under-immunized children. A before-and-after survey will assess changes in knowledge, attitudes, and practices among community health work-ers. Key informant interviews and focus groups with APS, health staff, and caregivers will explore feasibility, acceptability, and scalability of the new module. Quantitative data will be analyzed using descriptive statistics and regression models, including difference-in-differences analyses while controlling for underlying secular trends between districts over time. Qualitative data will be thematically analyzed. Results Data collection will occur from September 2025 to February 2026, with findings available by mid-2026. Conclusions This study will generate evidence on the effectiveness of a digital community health platform to reduce zero-dose prevalence in Mozambique. and inform programmatic scale-up and national policy.
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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 | 0.062 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".