Facility-Based Audit System With Integrated Community Engagement to Improve Maternal and Perinatal Health Outcomes in Rural Pakistan: Protocol for a Mixed Methods Implementation Study
Bibliographic record
Abstract
BACKGROUND: Maternal and newborn mortality in Pakistan remains as a major public health challenge. Pakistan faces significant infrastructure challenges and inadequate access to quality health care, exacerbated by sociocultural factors. Facility-based audit systems coupled with community engagement are key elements in achieving improved health system performance. We describe an implementation approach adapted from the World Health Organization audit cycle in real-world settings, with a plan to scale-up through mixed methods evaluation plan. OBJECTIVE: This study aims to implement a locally acceptable and relevant audit system and evaluate its feasibility within the rural health system of Pakistan for scale-up. METHODS: The implementation of the audit system comprises six phases: (1) identify facility and community leadership through consultative meetings with government district health offices, (2) establish the audit committee under the supervision of district health officer, (3) initiate audit with ongoing community engagement, (4) train the audit committee members, (5) launch the World Health Organization audit cycle (monthly meetings), and (6) quarterly review and refresher training. Data from all deliveries, live births, maternal deaths, maternal near misses, stillbirths, and neonatal deaths will be identified and recorded from four sources: (1) secondary-level care rural health facilities, (2) lady health workers' registers, (3) community representatives, and (4) project routine survey team. Concurrent quantitative and qualitative data will be drawn from case assessments, process analysis, and recommendations as components of iterative improvement cycles during the project. Outcomes will be the geographic distribution of mortality to measure the reach, proportion of facilities initiated to implement an audit system for measuring the adoption, proportion of audit committees with community representation, and proportion of audit committee members' sharing feedback regularly to measure acceptability and feasibility. In addition, outcomes of effectiveness will be measured based on data recording and reporting trends, identified modifiable factors for mortality and morbidity as underpinned by the Three Delays framework. Qualitative data will be analyzed based on perceived facilitators, barriers, and lessons learned for policy implications. Results will be summarized in frequencies and percentages and triangulated by the project team. Data will be analyzed using Stata (version 16; StataCorp) and NVivo (Lumivero) software. RESULTS: The study will be implemented for 20 months, followed by an additional 4-month period for follow-up. Initial results will be presented to the district health office and the District Health Program Management Team Meeting in the districts. CONCLUSIONS: This study will generate evidence about the feasibility and potential scale-up of a facility-based mortality audit system with integrated community engagement in rural Pakistan. Audit committees will complete the feedback loop linking health care providers, community representatives, and district health officials (policy makers). This implementation approach will serve decision makers in improving maternal and perinatal health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49578.
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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 | 0.096 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".