Health Facility Factors Influencing the Implementation of PTBI during the Provision of Intrapartum and Perinatal Care in Embu County
Notice bibliographique
Résumé
The objective of the study was to establish the health facility factors (HFF) influencing the implementation of PTBI during the provision of intrapartum and perinatal care in Embu County The study used a cross-sectional design. Random sampling technique was used to determine the sample size of 94 HCP, while the Purposive sampling technique was used to sample 24 client files (a quarter of the sample size) and 5 Key informants. The study was conducted in three hospitals in Embu County, Kenya. Questionnaires and document review guides were used to collect quantitative data and Key informant interview (KII) guides were used to collect qualitative data. Data analysis was done using SPSS version 21, descriptive statistics; Chi squires, Fisher’s test, and binary logistic model. Qualitative data were categorized into themes. Data findings were presented using tables and charts. Results: The findings in this study revealed that the majority of HCP in the maternity unit who agreed with the statement that adequacy of HFF influences implementation of PTBI were associated with the low implementation of PTBI as compared to those who disagreed. On the other hand, those HCP who agreed that there was adequate HFF were also associated with the low implementation of PTBI as compared to those HCP who disagreed. The findings further revealed there were inadequate staff, transport, and finances while drugs and equipment were adequate. The former three are important aspects in the implementation of PTBI and their inadequacy may lead to the low implementation of PTBI. In addition, this study also revealed that the highest number of respondents reported HFF affects the implementation of PTBI to a large extent as compared to those who reported moderately and low extent respectively. Respondents’ responses were echoed by the 5 KIIs of whom, four reported that the level of implementation is affected by HFF to a moderate and large extent respectively. In addition, the study recommends the county government consider improving HFF by; providing facilities for KMC, improving transport facilities, recruiting more skilled staff, and increasing funding in the field of midwifery/reproductive health to enhance the implementation of PTBI. Adequate funding will enhance staff recruitment, maintenance of transport, and timely procurement of drugs and equipment. Consequently, promoting the implementation of PTBI.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».