Critical maternal health knowledge gaps in low- and middle-income countries for the post-2015 era
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Notice bibliographique
Résumé
Effective interventions to promote maternal health and address obstetric complications exist, however 800 women die every day during pregnancy and childbirth from largely preventable causes and more than 90% of these deaths occur in low and middle income countries (LMIC). In 2014, the Maternal Health Task Force consulted 26 global maternal health researchers to identify persistent and critical knowledge gaps to be filled to reduce maternal morbidity and mortality and improve maternal health. The vision of maternal health articulated was comprehensive and priorities for knowledge generation encompassed improving the availability, accessibility, acceptability, and quality of institutional labor and delivery services and other effective interventions, such as contraception and safe abortion services. Respondents emphasized the need for health systems research to identify models that can deliver what is known to be effective to prevent and treat the main causes of maternal death at scale in different contexts and to sustain coverage and quality over time. Researchers also emphasized the development of tools to measure quality of care and promote ongoing quality improvement at the facility, district, and national level. Knowledge generation to improve distribution and retention of healthcare workers, facilitate task shifting, develop and evaluate training models to improve "hands-on" skills and promote evidence-based practice, and increase managerial capacity at different levels of the health system were also prioritized. Interviewees noted that attitudes, behavior, and power relationships between health professionals and within institutions must be transformed to achieve coverage of high-quality maternal health services in LMIC. The increasing burden of non-communicable diseases, urbanization, and the persistence of social and economic inequality were identified as emerging challenges that require knowledge generation to improve health system responses and evaluate progress. Respondents emphasized evaluating effectiveness, feasibility, and equity impacts of health system interventions. A prominent role for implementation science, evidence for policy advocacy, and interdisciplinary collaboration were identified as critical areas for knowledge generation to improve maternal health in the post-2015 era.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découle