Women Mobile Lifeline Channel Is a Key Stimulant of MCH Services Use in Resource Constrained Settings: A Success Story of Women Health Channel Uganda
Notice bibliographique
Résumé
Background Uganda has made progress in recent decades; however, the country still ranks among the top 10 countries in the world with high maternal, newborn, and child mortality rates. 336 women in every 100000 live births die due to preventable pregnancy related causes (under-five mortality rate 64/1000 live births; infant mortality rate 43/1000 live births; and neonatal mortality rate 27/1000 live births). Despite the growing global focus on reaching the last mile that necessitates the development of mHealth tools that best reach, empower, and mobilize the last mile women to seek and utilize critical and life-saving health care services as a vehicle for accelerating reduction of maternal and child deaths, mHealth tools in Uganda continue to limit focus on reporting and trucking of health indicators. Objective MIRA Channel is a single-window app with multiple channels on prenatal care, child immunization, newborn care, and family planning with the objective to improve maternal and child health outcomes in rural and resource-constrained settings. The app delivers information to women through interactive edutainment tools that builds on their knowledge, thus creating awareness on critical health issues and preempt timely use of MCH services. Methods Women Health Channel Uganda piloted the Women Mobile Lifeline Channel app in 15 public health facilities in Jinja district, Uganda, and particularly targeted pregnant women. A systematic review of records, particularly the health facility ANC register, was done to estimate the facility clientele size. Purposive random sampling was used to arrive at the survey sample. Two contact midwives and 5 VHTs were selected, trained, and given a connected mobile device at each of the implementing health facilities. Recruitment of women on the platform was done by VHTs using connected phones at community level, and 3489 pregnant women were studied for 16 months. Data was collected at baseline and at end line. Results Both at baseline and at end line, information on knowledge as well as usage of key MCH services was collected. All women had heard of ANC and the recommended place of delivery; however, only 59% at baseline had knowledge of the exact recommended number of ANC visits as opposed to 94% at end line. At baseline, 36% of women reported to have attended ANC 4 or more times at the most recent pregnancy as opposed to 82% at end line, while 63% of women at baseline reported to have given birth in a health facility for the previous pregnancy as opposed to 94% at end line. Sven neonatal deaths were reported in the cohort at baseline as opposed to 0 maternal deaths and 1 neonatal death at end line. Conclusions The pilot showed that one critical determinant of use of MCH services is the overall client knowledge and the perceived available support mechanism in the face of challenges. mHealth tools ought to expand focus to include stimulation of two-way mobile-based interactions that reinforce behavior change and preempt use as such. The Women Mobile Lifeline Channel that Women Health Channel is implementing offers lenses for Uganda and other countries to walk towards meaningful ICT integration in health.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».