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Enregistrement W4220946996 · doi:10.18203/2394-6040.ijcmph20220839

Determinants of skilled birth attendants in Nepal: a case of Surkhet district

2022· article· en· W4220946996 sur OpenAlexaboutno aff
Sunil Kumar Shah, José Augusto Simões

Notice bibliographique

RevueInternational Journal of Community Medicine and Public Health · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésChildbirthMedicineIncentiveQuarter (Canadian coin)Birth attendantPovertyPopulationHealth facilitySocioeconomicsEnvironmental healthNursingPregnancyGeographyMaternal healthEconomic growthHealth services

Résumé

récupéré en direct d'OpenAlex

Background: Safe delivery incentive program was introduced to increase the skilled attendants at births. The program provided childbirth by skilled birth attendants as well as incentives to skilled birth attendants ‘cash’ to women giving birth in a health facility in addition to incentives to health provider for each delivery attended, either at home or the facility. Due to its implementation and administrative delays, the program was reformed and implemented as a ‘safer mother program’ popularly known as “aama-suraksha-karyakram” since January 2009.Methods: The study was conducted in Surkhet district of Nepal. Surkhet is a hilly district and is head-quarter of mid-western development region of Nepal. There is one hospital, 5 PHCCs, 9 HPs and 38 SHPs serving 288,527 people in the district. The delivery by trained health worker (HW) in the district is 31.8% in 2005/06 which has increased about two times for two years. Surkhet is one of the districts monitoring the process indicators for safe motherhood programme in Nepal. Birth preparedness package programme has been implementing in the district from this year. The study population were the mothers within the age group of 15 to 49 years in Surkhet district. The sampling frame of the study was the mothers who had delivered the baby within 12 months preceding the survey.Results: About one third mothers, having 0-5 poverty score, utilised delivery assisted by HWs, while about three fourth of them having more than 5 score utilised HWs as delivery assistant. Higher educated mothers utilised HWs as delivery assistant more than that of higher educated husband. Among higher educated mothers, about 85% utilised delivery assisted by HWs, while it was about 75% for higher educated husband. Occupation of mother was also significantly associated with utilisation of delivery by HWs. Mother having office work utilised about 5 times higher HWs than others as their delivery assistant. The distance to health facility was significantly associated with utilisation of delivery attendant (p value <0.001). The mothers with less travelling time to reach health facility were more likely to utilise HWs as delivery attendant. About three fourth mothers who needed less than half an hour utilised delivery assisted by HWs. There was equal proportion of mothers who needed 30-59 minutes to reach the nearest health facility. In the other hand, about 73% of mothers who needed one hour or more to reach health facility utilised others as delivery assistant. Perceived quality of service to nearby health facility by mothers was also significantly associated with utilisation of HWs as delivery attendant (p value <0.05). About two third of mothers perceiving good quality of service at local health facility utilised HWs as delivery attendant while, it was only 44% among mothers perceiving poor quality of services.Conclusions: There should be adequate planning and preparation at all levels of health facilities; implementing a new program should not adversely affect another existing service delivery system. For the optional implementation, hospital organogram should be revised; and physical facilities and the low-risk birthing-centers with referral linkages should be expanded.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,037
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,001
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,071
Tête enseignante GPT0,401
Écart entre enseignants0,330 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2022
Routes d'admission1
Résumé présentoui

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