NAUNEHAL; Integrated immunization and MNCH interventions: A quasi-experimental study–Protocol
Bibliographic record
Abstract
Great improvements in the health of newborns, children, and women in Pakistan are needed. A large body of literature has demonstrated that the majority of maternal, newborn, and child deaths are preventable with essential health strategies including immunization, nutrition interventions, and child health interventions. Despite the importance of these interventions for the health of women and children, access to services continues to be a barrier. Furthermore, demand for services also contributes to low coverage of essential health interventions. Given the emerging threat of COVID-19 coupled with already weak maternal and child health, delivering effective and feasible nutrition and immunization services to communities, and increasing demand and uptake of services is a pressing and important need.This quasi-experimental study aims to improve health service delivery and increase uptake. The study included four main intervention strategies including community mobilization, mobile health teams offering MNCH and immunization services, engagement of the private sector, and testing of a comprehensive health, nutrition, growth, and immunization app, Sehat Nishani, for a period of 12 months. The target group of the project were women of reproductive age (15-49 years) and children under-five. The project was implemented in three union councils (UCs) in Pakistan including Kharotabad-1(Quetta District, Balochistan), Bhana Mari (Peshawar District, Khyber Pakhtunkhwa) and Bakhmal Ahmedzai (Lakki Marwat district, Khyber Pakhtunkhwa). Propensity score matching based on size, location, health facilities, and key health indicators of UC was conducted to identify three matched UCs. A household baseline, midline, endline and close-out assessment will be conducted for evaluating coverage of interventions as well as the knowledge, attitude, and practices of the community in the MNCH and COVID-19 context. Descriptive and inferential statistics will be used to test hypotheses. As well, a detailed cost-effectiveness analysis will be conducted to generate costing data for these interventions to effectively inform policymakers and stakeholder on feasibility of the model. Trial registration: NCT05135637.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".