Lessons learnt in the response to COVID-19 in Mozambique: enabling readiness for the next pandemic
Notice bibliographique
Résumé
Introduction: The coronavirus disease 2019 (COVID-19) has led to a dramatic loss of human lives worldwide and caused economic and social disruptions. The risk of another pandemic occurring is ever-present requiring countries to document factors that influenced the response to COVID-19 to guide the response to future pandemics. This study documents lessons learnt from Mozambique's COVID-19 response, considering the perspectives of various stakeholders and examining different components of the response. Methods: We used a qualitative phenomenology research design and collected data using in-depth interviews. We used purposive sampling by selecting institutions with relevant experience and knowledge to inform the study objectives. We also used snowballing techniques by asking respondents for other potential informants. We interviewed 19 individuals indicated by the representatives of the institutions selected for the study. The institutions were mostly based in Maputo city, the country's capital. Participants were asked about their role in the organization; responsibility in vaccine distribution and delivery in Mozambique; their opinion on what worked well in the country's response to COVID-19, and what could be improved as preparation to future pandemics. Data was coded using a computer-assisted qualitative data analysis software Maxqda 2020 and analyzed using a deductive thematic approach. A validation meeting was held, in which research participants were asked to check the accuracy of the results and interpretations. Results: Key drivers of the COVID-19 response were strong leadership; a clear plan and strategies; a functional coordination mechanism; the use of evidence to make decisions; a careful consideration of priority groups; investments in the supply chain and surveillance systems; the utilization of pre-existing vaccination structures; and partnership between the government and several stakeholders. There is room for improvement including the development of a clear budget, a communication plan, creation of an emergency fund, accountability in the use of funds, decentralization of surveillance infrastructure and representation of vulnerable, marginalized, and hard-to-reach populations in the design and implementation of pandemic response. Conclusion: The lessons learned from the COVID-19 response in Mozambique, which could be considered when preparing for an effective and equitable response to future pandemics, are in essence the following: there should be government leadership, a response plan, adequate resources, use of data to inform decisions, constant vigilance, a prompt response, involvement of all stakeholders and documentation of actions for continuous learning. These lessons could improve pandemic preparedness nationally and globally.
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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 | 0,009 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,009 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».