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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,096 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,040 | 0,006 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».