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Enregistrement W1919860429 · doi:10.2196/mhealth.4208

Potential Roles of Mhealth for Community Health Workers: Formative Research With End Users in Uganda and Mozambique

2015· article· en· W1919860429 sur OpenAlexvenueno aff
Meelan Thondoo, Daniel Strachan, Maureen Nakirunda, Sozinho Ndima, Abel Muiambo, Karin Källander, Zelee Hill

Notice bibliographique

RevueJMIR mhealth and uhealth · 2015
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesLondon School of Hygiene and Tropical MedicineMedical Research CouncilBill and Melinda Gates Foundation
Mots-clésmHealthPsychological interventionThematic analysisFocus groupFormative assessmentCommunity healthShort Message ServicePhoneMedical educationQualitative researchPsychologyApplied psychologyNursingMedicineBusinessPublic healthComputer scienceMarketingSociology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Community health workers are reemerging as an essential component of health systems in low-income countries. However, there are concerns that unless they are adequately supported, their motivation and performance will be suboptimal. mHealth presents an opportunity to improve support for community health workers; however, most interventions to date have been designed through a top-down approach, rarely involve the end user, and have not focused on motivation. OBJECTIVE: To use formative research to explore the views of community health workers in Uganda and Mozambique on the potential role of mHealth in their work delivering integrated community case management of children. METHODS: We conducted 24 in-depth interviews and 5 focus group discussions with community health workers in Uganda and Mozambique. Data were collected on: current phone use, preferred phone and charger characteristics, and perceptions of a range of potential mHealth interventions. Interviews were conducted in the local language, were audio recorded and converted into expanded notes. Interviews were coded for key thematic areas using both deductive and inductive codes. Deductive codes included mHealth's potential impact on motivation and performance. RESULTS: The most salient roles of mHealth in improving performance and motivation were reducing the need for travel, improving efficiency and planning, receiving feedback and information, and improving communication with supervisors and other community health workers. This was mostly through improved voice and short message service (SMS) text communication. Specific components of mHealth interventions that participants felt could improve motivation included increasing their visibility and credibility through branding of phones; providing an SMS response to data submission; and sending SMS messages about the importance of their work and achievements, rather than just reminders or technical messages. Participants identified feasibility issues related to the language of SMS messages, network coverage, and the need for a balance between phone function and battery life. Phones with a dual SIM cards would ameliorate network problems but would reduce battery life. The provision of a solar charger was viewed as beneficial. CONCLUSIONS: Conducting formative research with end users is likely to improve mHealth interventions by: (1) identifying interventions that are likely to have the greatest impact and be the most acceptable, (2) developing salient SMS messages, and (3) identifying feasibility issues. mHealth interventions also could have an important impact on health worker motivation, which should be considered by intervention developers and in evaluations, especially as small modifications could have a significant impact. Our study suggests that using phones to improve direct communication should be considered, even when planners aim to focus on the provision of a specific application.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,017
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,183
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0170,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,203
Tête enseignante GPT0,525
Écart entre enseignants0,322 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations61
Publié2015
Routes d'admission1
Résumé présentoui

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