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Enregistrement W3130291300 · doi:10.3886/e123302v1

Increasing Women's Access to Skilled Pregnancy Care in Rural Nigeria

2015· dataset· en· W3130291300 sur OpenAlexaffabout

Notice bibliographique

RevueICPSR Data Holdings · 2015
Typedataset
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésPregnancyBusinessLabour economicsObstetricsMedicineEconomicsBiology

Résumé

récupéré en direct d'OpenAlex

Nigeria is estimated to account for 19% of all estimated global maternal deaths with approximately 58,000 in 2015. The high number is partly due to the inadequate access of women to evidence-based skilled pregnancy care. The Federal Ministry of Health (FMoH) and all major health policy agencies in Nigeria have recognized the need for increased access to skilled obstetric care, especially in rural areas, as critical to reducing the high rate of maternal mortality. However, despite the fact that policymakers recognize that primary health care should play a key role in improving rural women's access to skilled pregnancy care, Primary Health Centres (PHCs) are often poorly utilized throughout the country. This project is a 5-year (2015-2020) implementation research conducted by the Women's Health and Action Research Centre (WHARC), Benin City, Nigeria in collaboration with the University of Ottawa (UOttawa), Canada and with funding from the International Development Research Centre (IDRC), Global Affairs Canada (GAC) and the Canadian Institute for Health Research (CIHR) under the Innovating for Maternal and Child Health in Africa (IMCHA) Initiative. The project's specific objectives are: 1) to identify the demand and supply factors responsible for the use and non-use of PHCs for pregnancy care in Esan South East and Etsako East LGAs of Edo State, Nigeria; 2) based on Objective 1, to derive and implement a set of multi-faceted community-led interventions to increase women's access to skilled pregnancy care offered in PHCs in Esan South East and Etsako East Local Government Areas (LGA); and 3) to evaluate the effectiveness of the interventions using both indicators of access to services, as well as maternal and fetal/newborn health outcomes in the intervention communities. The study was conducted in Esan South East and Etsako East Local Government Areas (LGAs) in Edo State in southern Nigeria. Both LGAs are located in the rural and riverine areas of the state, adjacent to River Niger, with Estako East in the northern part of the Edo State part of the river, while Esan South East is in the southern part. Edo State is one of Nigeria’s thirty-six states. Each state consists of LGAs, and LGAs consist of political/health Wards. The study was originally designed to be a randomized control trial (Yaya et al., 2018) but was changed to a quasi-experiment separate sample pretest and posttest design. The change was necessitated by the difficulty in achieving reliable randomization in the study communities. The study was conducted in three phases. At phase one, a baseline was conducted using a mixed-method approach to address objective 1. Based on the results of the baseline research, a set of intervention activities were designed and implemented simultaneously in phase 2 for two years. Phase three was the endline research which addressed the study objective 3. Ethical approval for the study was obtained from the National Health Research Ethics Committee (NHREC) of Nigeria – protocol number NHREC/01/01/2007 – 10/04/2017; and written informed consent was obtained from individual respondent/participant, except in the community conversations where the consent was verbal. The data we are sharing contain baseline and endline data. collected through a mixed-method approach to address the study objectives. The baseline data were collected between July 29 to August 16, 2017, using a mixed-method that comprises a household survey, exit interview, PHC site assessment survey, community conversation, focus group discussion, and key informant interview. The endline data were collected between June 24 and July 6, 2020, using a household survey. All the data collection instruments were pretested and the data were collected by trained data collectors.

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)0,001
score de la tête « metaresearch » (Gemma)0,002
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: aucune
GenreSignal candidat: Jeu de données · Signal consensuel: aucune
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,037

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,002
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,034
Tête enseignante GPT0,354
Écart entre enseignants0,320 · 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
GenreJeu de données

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

En bref

Citations0
Publié2015
Routes d'admission2
Résumé présentoui

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