Quality of Labour and Delivery Care Process and Associated Factors in Government Hospitals of Ethiopia: a multilevel analysis
Notice bibliographique
Résumé
Abstract Background Ethiopia has one of the highest maternal mortality ratios in Africa. Few have examined the quality of labour and delivery (L&D) care in the country. This study evaluated the quality of routine L&D care and identified patient-and hospital-level factors associated with the quality of care in a subset of government hospitals. Materials and methods This was a facility-based, cross-sectional study using direct non-participant observation carried out in 2016. All mothers who received routine L&D care services at government hospitals (n=20) in one of the populous regions of Ethiopia, Southern Nations Nationalities and People’s Region (SNNPR), were included. Mixed effects multilevel linear regression modeling was employed in two stages using hospital as a random effect, with quality of L&D care as the outcome and selected patient and hospital characteristics as independent variables. Patient characteristics included woman’s age, number of previous births, number of skilled attendants involved in care process, and presence of any danger sign in current pregnancy. Hospital characteristics included teaching hospital status, mean number of attended births in the previous year, number of fulltime skilled attendants in the L&D ward, whether the hospital had offered refresher training on L&D care in the previous 12 months, and the extent of resources available (measured on a 0-100% scale) to provide quality L&D care as defined by the Ethiopian Ministry of Health in 2014. The outcome was measured with a quality of L&D care score (scale 0 to 100) based on adherence to L&D care standards, which had been introduced by the Ethiopian Ministry of Health in 2014. Results On average, the hospitals met two-thirds of the standards for L&D care quality, with substantial variation between hospitals (standard deviation 10.9 percentage points). While the highest performing hospital met 91.3% of standards, the lowest performing hospital met only 35.8% of the standards. Hospitals had the highest adherence to standards in the domain of immediate and essential newborn care practices (86.8%), followed by the domain of care during the second and third stages of labour (77.9%). Hospitals scored substantially lower in the domains of active management of third stage of labour (AMTSL) (42.2%), interpersonal communication (47.2%), and initial assessment of the woman in labour (59.6%). We found the quality of L&D care score was significantly higher for women who had a history of any danger sign (β = 5.66; p-value = 0.001) and for women who were cared for at a teaching hospital (β = 12.10; p-value = 0.005). Additionally, hospitals with lower volume and more resources available for L&D care (P-values < 0.01) had higher L&D quality scores. Conclusions Overall, the quality of L&D care provided to labouring mothers at government hospitals in SNNPR was limited. Lack of adherence to standards in the areas of the critical tasks of initial assessment, AMTSL, interpersonal communication during L&D, and respect for women’s preferences are especially concerning. Without greater attention to the quality of L&D care, regardless of how accessible hospital L&D care becomes, maternal and neonatal mortality rates are unlikely to decrease substantially.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 source (Gemma direct ou Codex distillé), 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 ».