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Enregistrement W2046367076 · doi:10.1016/j.ijgo.2014.07.001

Looking beyond the Millennium Development Goal deadline for women and newborns: Evidence is needed for action on implementation of quality care

2014· editorial· en· W2046367076 sur OpenAlexaboutno aff
Judith A. Fortney

Notice bibliographique

RevueInternational Journal of Gynecology & Obstetrics · 2014
Typeeditorial
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMillennium Development GoalsMedicineContext (archaeology)Psychological interventionQuarter (Canadian coin)PopulationInfant mortalityDeveloping countryEconomic growthEnvironmental healthNursing

Résumé

récupéré en direct d'OpenAlex

This issue of the International Journal of Gynecology and Obstetrics (IJGO) introduces a new set of pages on “Evidence for Action” for maternal and newborn health (MNH) and survival. Given the significant increase in births in facilities and with skilled providers, quality of care has never been so important in the last push toward the Millennium Development Goals’ (MDGs) deadline and beyond. Without improvements in quality of care, avoidable deaths will continue. Without better evidence, and use of evidence on quality of care, the implementation of known interventions at scale cannot be realized. While these pages are, to a degree, modelled on the Averting Maternal Death and Disability (AMDD) pages published in IJGO between 2000 and 2011, the new pages will give greater attention to political context and the process of implementing improvements in care. Although globally MDGs 4 and 5 are unlikely to be achieved, after a quarter century of the safe motherhood movement, levels of maternal and newborn mortality have decreased and are continuing to decrease. However, gaps are increasing between Sub-Saharan Africa and other countries. Thirteen percent of the world's population live in Sub-Saharan Africa, but 62% of maternal deaths [1] and a quarter (28%) of newborn deaths occur in this region [2]; and 38 of the 48 Sub-Saharan African countries have maternal mortality ratios greater than 300 deaths per 100 000 live births [1]. For many of these countries progress has been slow since 1990 and both neonatal mortality and stillbirth rates remain high [3]. This is due to continuing poor quality of care at birth for newborns. The majority of these maternal and newborn deaths are avoidable and can be tackled by improving care for both women and newborns around the time of birth. Despite this, too little progress has been made over the last 25 years. There is a growing consensus that lack of political will and focus, weak governance, and lack of effective accountability mechanisms at all levels have contributed to poor progress in this region. In spite of great strides in understanding why these deaths occur and how to prevent them, a lag in implementation remains and knowledge has not produced markedly improved outcomes. Although the means of improvement have been known for decades, it is widely believed that countries lack the political will to implement the knowledge to improve the well-being of a population with little voice—women and children. It is in this context that the UK Government has funded Evidence for Action (E4A), a five-year program (2012 − 2016) in six African countries with the worst MNH outcomes in the region (Ethiopia, Ghana, Malawi, Nigeria, Sierra Leone, and Tanzania). E4A's approach is markedly different from previous international development investments by the UK Government and presents further opportunity for implementation science. At the heart of the program's design is the hypothesis that strategically packaged evidence will inspire and fuel effective and targeted advocacy and accountability efforts that will result in decisions and actions to change the level, distribution, and use of resources and so improve the availability and quality of MNH services necessary to save maternal and newborn lives. Evidence of success in other sectors within health—such as smoking [4], tuberculosis, polio, and malaria [5]—strongly suggests that inputs aimed at strengthening the interaction between evidence, accountability, and advocacy can achieve the changes in political will and priority necessary to result in decisions and actions. The E4A series of papers in IJGO will present the means to increase implementation of proven methods in MNH. Selected sets of articles that will be included over the next two years, twice yearly, will contain articles highlighting the use of evidence to fuel implementation through advocacy or accountability mechanisms. This will also include a few shorter pieces from the E4A country offices presenting documented examples of how evidence is being mobilized to move political will and improve quality of care. In this current issue, the articles introduce readers to the E4A program's theoretical framework and activities [6], present the methods used to collect baseline monitoring data on evidence of political will and the use of evidence [7], the use of scorecards to assess facility readiness for delivering emergency obstetric and newborn care [8], and the establishment of a country level accountability mechanism [9]. Articles in future issues will focus on issues such as quality of care, human resources, and accountability in maternal health. Contributors to these selected papers will be invited from E4A teams and other collaborating international and in-country organizations including government, UN agencies, academics, civil society actors, and service providers. In the face of competing priorities, both globally and nationally it is important not to lose focus on the unmet MDGs 4 and 5. The Partnership for Maternal, Newborn and Child Health at the global level and the Campaign on Accelerated Reduction of Maternal, Newborn and Child Mortality in Africa (CARMMA) at the regional level are making concerted efforts to ensure the survival of mothers and newborns. The efficiency of global resource use for these improvements has to be fueled by evidence that is powerful, and can inform and stimulate action.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,031
score de la tête « metaresearch » (Gemma)0,134
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,166

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0310,134
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0050,004
Études des sciences et des technologies0,0030,006
Communication savante0,0160,016
Science ouverte0,0030,005
Intégrité de la recherche0,0110,019
Charge utile insuffisante (le modèle a refusé de juger)0,0230,006

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.

Tête enseignante Opus0,033
Tête enseignante GPT0,403
Écart entre enseignants0,370 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2014
Routes d'admission1
Résumé présentoui

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