Evaluation of the Development, Implementation, Maintenance, and Impact of 3 Digital Surveillance Tools Deployed in Malawi During the COVID-19 Pandemic: Protocol for a Modified Delphi Expert Consensus Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has highlighted the importance of strengthening national monitoring systems to safeguard a globally connected society, especially those in low- and middle-income countries. Africa's rapid adoption of digital technological interventions created a new frontier of digital advancement during crises or pandemics. The use of digital tools for disease surveillance can assist with rapid outbreak identification and response, handling duties such as diagnosis, testing, contact tracing, and risk communication. Malawi was one of the first countries in the region to launch a government-led coordinated effort to harmonize and streamline the necessary COVID-19 digital health implementation through an integrated system architecture. OBJECTIVE: The aim of this study is to seek expert consensus using the Delphi methodology to examine Malawi's COVID-19 digital surveillance response strategy and to assess the digital tools using the World Health Organization mHealth (mobile health) Assessment and Planning for Scale (MAPS) toolkit. METHODS: This protocol follows the Guidance on Conducting and REporting DElphi Studies. Participants must have first-hand experience on the design, implementation or maintenance with COVID-19 digital surveillance systems. There will be no restrictions on the level of expertise or years of experience. The panel will consist of approximately 40 participants. We will use a modified Delphi process whereby rounds 1 and 2 will be hosted online by Qualtrics and round 3 will encompass a face-to-face workshop held in Malawi. Consensus will be defined as ≥70% of participants strongly disagree, disagree, or somewhat disagree, or strongly agree, agree, or somewhat agree. During round 3, the face-to-face workshop, participants will be asked to complete, the MAPS toolkit assessment on the digital tool on which they are experts. The MAPS toolkit will enable the panel members to assess the digital tools from a sustainable perspective from six distinct, yet complementary axes: (1) groundwork, (2) partnerships, (3) financial health, (4) technology and architecture, (5) operations, and (6) monitoring and evaluation. RESULTS: The ability of a country to collate, diagnose, monitor, and analyze data forms the cornerstone of an efficient surveillance system, allowing countries to plan and implement appropriate control actions. Malawi was one of the first countries in the African region to launch a government-led coordinated effort to harmonize and streamline the necessary COVID-19 digital health implementation through an integrated system architecture. CONCLUSIONS: We anticipate findings from this Delphi study will provide insights into how and why Malawi was successful in deploying digital surveillance systems. In addition, findings should produce recommendations and guidance for the rapid development, implementation, maintenance, and impact of digital surveillance tools during a health crisis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58389.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».