Effectiveness of Neonatal Resuscitation Training Programs, Implementation, and Scale-Up in Low- and Middle-Income Countries
Notice bibliographique
Résumé
INTRODUCTION: To describe recent evidence regarding the most effective neonatal resuscitation training program and scale-up of these programs in low- and middle-income countries (LMICs), which has contributed to the upcoming Lancet Global Newborn Care Series 2025, and forms part of a supplement describing an extensive synthesis on effective newborn interventions in LMICs. METHODS: We included relevant studies from Medline, Embase, CINAHL, Cochrane CENTRAL and Global Index Medicus databases on the effectiveness and scale-up of Neonatal Resuscitation Training Programs (NRTP), with searches run August 2022. Data extraction and quality assessments were completed independently and in duplicate. RESULTS: A total of 93 unique records met the eligibility criteria and were included in our analyses across the reviews. NRTPs improved most knowledge and skill-based outcomes but impact on mortality varied. Included studies identified knowledge and skill retention, standardized training protocols, and limited training opportunities for health care providers as challenges to current NRTPs. CONCLUSION: Reported knowledge, skills, and mortality outcomes were similar across NRTPs. The Helping Babies Breathe (HBB) program was found to be cost-effective in Tanzania, suggesting that the HBB program or elements thereof are low-cost and scalable in LMICs. Future research across diverse settings should evaluate the cost-effectiveness of other NRTPs. To scale-up current NRTPs, programs should focus on improving long-term retention outcomes and improving training material accessibility. INTRODUCTION: To describe recent evidence regarding the most effective neonatal resuscitation training program and scale-up of these programs in low- and middle-income countries (LMICs), which has contributed to the upcoming Lancet Global Newborn Care Series 2025, and forms part of a supplement describing an extensive synthesis on effective newborn interventions in LMICs. METHODS: We included relevant studies from Medline, Embase, CINAHL, Cochrane CENTRAL and Global Index Medicus databases on the effectiveness and scale-up of Neonatal Resuscitation Training Programs (NRTP), with searches run August 2022. Data extraction and quality assessments were completed independently and in duplicate. RESULTS: A total of 93 unique records met the eligibility criteria and were included in our analyses across the reviews. NRTPs improved most knowledge and skill-based outcomes but impact on mortality varied. Included studies identified knowledge and skill retention, standardized training protocols, and limited training opportunities for health care providers as challenges to current NRTPs. CONCLUSION: Reported knowledge, skills, and mortality outcomes were similar across NRTPs. The Helping Babies Breathe (HBB) program was found to be cost-effective in Tanzania, suggesting that the HBB program or elements thereof are low-cost and scalable in LMICs. Future research across diverse settings should evaluate the cost-effectiveness of other NRTPs. To scale-up current NRTPs, programs should focus on improving long-term retention outcomes and improving training material accessibility.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».