Evaluating a Mobile Phone–Delivered Text Message Reminder Intervention to Reduce Infant Vaccination Dropout in Arua, Uganda: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Globally, suboptimal vaccine coverage is a public health concern. According to Uganda's 2016 Demographic and Health Survey, only 49% of 12- to 23-month-old children received all recommended vaccinations by 12 months of age. Innovative ways are needed to increase coverage, reduce dropout, and increase awareness among caregivers to bring children for timely vaccination. OBJECTIVE: This study evaluates a personalized, automated caregiver mobile phone-delivered text message reminder intervention to reduce the proportion of children who start but do not complete the vaccination series for children aged 12 months and younger in select health facilities in Arua district. METHODS: A two-arm, multicenter, parallel group randomized controlled trial was conducted in four health facilities providing vaccination services in and around the town of Arua. Caregivers of children between 6 weeks and 6 months of age at the time of their first dose of pentavalent vaccine (Penta1; containing diphtheria, tetanus, pertussis, hepatitis B, and Haemophilus influenzae type b antigens) were recruited and interviewed. All participants received the standard of care, defined as the health worker providing child vaccination home-based records to caregivers as available and providing verbal instruction of when to return for the next visit. At the end of each day, caregivers and their children were randomized by computer either to receive or not receive personalized, automated text message reminders for their subsequent vaccination visits according to the national schedule. Text message reminders for Penta2 were sent 2 days before, on the day of, and 2 days after the scheduled vaccination visit. Reminders for Penta3 and the measles-containing vaccine were sent on the scheduled day of vaccination and 5 and 7 days after the scheduled day. Study personnel conducted postintervention follow-up interviews with participants at the health facilities during the children's measles-containing vaccine visit. In addition, focus group discussions were conducted to assess caregiver acceptability of the intervention, economic data were collected to evaluate the incremental costs and cost-effectiveness of the intervention, and health facility record review forms were completed to capture service delivery process indicators. RESULTS: Of the 3485 screened participants, 1961 were enrolled from a sample size of 1962. Enrollment concluded in August 2016. Follow-up interviews of study participants, including data extraction from the children's vaccination cards, data extraction from the health facility immunization registers, completion of the health facility record review forms, and focus group discussions were completed by December 2017. The results are expected to be released in 2021. CONCLUSIONS: Prompting health-seeking behavior with reminders has been shown to improve health intervention uptake. Mobile phone ownership continues to grow in Uganda, so their use in vaccination interventions such as this study is logical and should be evaluated with scientifically rigorous study designs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04177485; https://clinicaltrials.gov/ct2/show/NCT04177485. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/17262.
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 | 0,022 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,049 | 0,006 |
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 ».