When, where and who? Accessing health facility delivery care from the perspective of women and men in Tanzania: a qualitative study
Notice bibliographique
Résumé
BACKGROUND: Childbirth is a momentous event for women and their partners, yet women continue to die in childbirth worldwide, particularly in sub-Saharan Africa. To reduce maternal mortality and increase the number of women delivering at health facilities, it is important to understand reasons why women who do deliver at health facilities chose to do so. Therefore, the objective of this qualitative study was to explore the perceptions of women and men on (i) when women go to the hospital; (ii) where women deliver; and (iii) who is involved in the delivery process related to accessing health facilities for delivery care in Tanzania. METHODS: Using a qualitative design, four focus group discussions (n = 23) and semi-structured interviewers (n = 12) were held with postnatal women and men who were attending a postnatal clinic in the Lake Zone region of Tanzania. Data was analyzed using thematic coding. RESULTS: Women and men expressed factors that influenced when, where, and with whom they accessed health facilities for delivery care, with the quality of care received providing a significant influence. When decisions were made about going to the hospital, there were challenges that resulted in delayed treatment seeking; however, couples recognized the need to seek care earlier to prevent complications. Private hospitals were the preferred location for delivery with public hospitals and home deliveries with traditional birth attendants being less desirable. Both when and where delivery took place was influenced by the desire for better quality of care received as well as financial costs. Finally, there was mixed evidence on who was involved in decision making around delivery location from the perspective of women and men, but both groups expressed a preference for more male involvement during the delivery. CONCLUSION: Men and women show desire for women to delivery at health facilities; however, improvements are needed with respect to maternal care and humanizing the birth process in Tanzania. Greater emphasis needs to be placed on including men during the birth process, improving the quality of care received in public hospitals, and reducing the barriers to accessing health facilities for delivery care.
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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 | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».