How can health systems better prepare for the next pandemic? A qualitative study of lessons learned from the COVID-19 response in Nigeria
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Notice bibliographique
Résumé
Fragile health systems can become overwhelmed during public health crises, further exacerbating the human, economic, and political toll. It is then necessary as a country, to assess, understand, document, and report the activities/measures that are considered nationally and sub-nationally significant, both in terms of COVID-19 responses and strengthening of the health system for the future. Data collection was through a scoping review of 198 publications that were comprised of official documents, journal articles, and media reports that were published from December 2019 to December 2020. Journal articles were sourced from online journals in PubMed, Google Scholar, and Scopus using search terms/queries. Published official documents were retrieved from relevant websites of government agencies and development partners and media searches were performed in FACTIVA. In addition, qualitative data using in-depth interviews of key informants were collected from 38 respondents in April 2022. The transcripts from the IDIs were coded, and thematic analysis and narrative synthesis of data were done using NVivo version 12 using Palagyi et al.’s framework. Our findings revealed the need to institutionalize some COVID-19 response activities and to efficiently prioritize financial and material resources during a pandemic response. Also, to introduce flexibility in financial response activities. Pooling and funds management was found useful but the integration of response activities into already existing epidemic response pillars must be prioritized. Research should be incorporated early in pandemic responses. The need to use evidence in decision-making and include all levels of government in planning response actions was found necessary to enhance trust and compliance. This study demonstrates the value of applying the Palagyi et al. framework on systems preparedness towards emerging infectious diseases to understand how the health systems can be better prepared for the next pandemic. It highlighted specific strengths and areas of potential growth for pandemic response in Nigeria.
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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 | 0,029 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| 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 découle