Impact of a Multicomponent Intervention to Build Capacity of Public Health Workers to Make Algorithmic Diagnosis and Management of High-Risk Pregnancies in Uttar Pradesh, India: Protocol for a Matched-Control, Before-After, Quasi-Experimental Study With a Mixed Methods Design
Notice bibliographique
Résumé
BACKGROUND: In India, 20-30% pregnancies fall under high-risk category, contributing to 75% of perinatal mortality and morbidity. An effective approach to reduce maternal and neonatal mortality/morbidity is early identification, effective management, and timely referral of high-risk pregnancies (HRPs). The Integrated High-Risk Pregnancy Tracking and Management (IHRPTM) program aims to enhance capacity of auxiliary nurse midwives (ANMs), medical officers (MOs), and specialist gynaecologists by: i. providing algorithmic, color-coded, detailed (yet simple) protocols for six HRP conditions, customized for each role, ii. offering live training, iii. delivering digital training and hand-holding, and iv. facilitating tracking pregnancies and management of HRPs. Equipping health workers (HWs) on these interventions facilitates early identification, effective management, and timely referrals, ultimately improving primary care and satisfaction of mothers with HRPs. Stated interventions are implemented in the intervention arm for 18 months, while during this period, HWs of intervention and control arms will receive routine training through state and national programs, ensuring pregnant women have equal access to routine maternity services. OBJECTIVE: At the system level, the program evaluates the impact on improvement in the knowledge and skills of HWs in diagnosing and managing HRPs. At the community level, it assesses the translation of this knowledge into practice, in terms of early diagnosis and effective management, among women with HRPs. METHODS: The program will be implemented in two intervention districts (Sambhal and Shravasti) and two matched control districts (Baduan and Gonda) of Uttar Pradesh, on six HRPs. Study uses a 'quasi-experimental, before-and after trial design', with intervention and control arms. However, impact of program will be assessed only on three HRPs: moderate/severe anaemia, pregnancy-induced hypertension, and antepartum haemorrhage (APH), including placenta previa/abruptio placenta. System level impacts will be assessed through qualitative data collected from district officials, specialist gynaecologists, MOs and ANMs, at baseline and endline. Community level outcomes will be measured quantitatively using baseline and endline data from recently delivered women (RDW), using difference-in-difference (DiD) technique. RESULTS: The impact evaluation protocol was approved by ARMMAN's Scientific Review Board and Sigma's Institutional Review Board. The protocols for six HRP-conditions were vetted by the government of Uttar Pradesh. By November 2024, all the ANMs, MOs, specialist gynaecologists, staff nurses, and community health officers in two intervention districts were trained on six HRP-protocols. Digital learning tool and WhatsApp support system was also introduced to facilitate continued learning and handholding of ANMs in managing HRPs and/or to clear doubts. Pre-intervention/baseline data was collected from two arms, during June-October 2024. CONCLUSIONS: This trial will provide valuable insights into the feasibility and effectiveness of the program, at system and community levels, in a low resource setting like Uttar Pradesh. If successful, these insights can feed into capacitating HWs, at scale, in all the districts and expansion to other HRPs, with significant potential for improving maternal and neonatal outcomes of the state.
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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 | 0,014 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,002 |
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 ».