Saving millions of lives but some resources squandered: emerging lessons from health research system pandemic achievements and challenges
Notice bibliographique
Résumé
During the SARS-CoV-2 pandemic, astonishingly rapid research averted millions of deaths worldwide through new vaccines and repurposed and new drugs. Evidence use informed life-saving national policies including non-pharmaceutical interventions. Simultaneously, there was unprecedented waste, with many underpowered trials on the same drugs. We identified lessons from COVID-19 research responses by applying WHO's framework for research systems. It has four functions-governance, securing finance, capacity-building, and production and use of research-and nine components. Two linked questions focused the analysis. First, to what extent have achievements in knowledge production and evidence use built on existing structures and capacity in national health research systems? Second, did the features of such systems mitigate waste? We collated evidence on seven countries, Australia, Brazil, Canada, Germany, New Zealand, the United Kingdom and the United States, to identify examples of achievements and challenges.We used the data to develop lessons for each framework component. Research coordination, prioritization and expedited ethics approval contributed to rapid identification of new therapies, including dexamethasone in the United Kingdom and Brazil. Accelerated vaccines depended on extensive funding, especially through the Operation Warp Speed initiative in the United States, and new platforms created through long-term biomedical research capacity in the United Kingdom and, for messenger ribonucleic acid (mRNA) vaccines, in Canada, Germany and the United States. Research capacity embedded in the United Kingdom's healthcare system resulted in trial acceleration and waste avoidance. Faster publication of research saved lives, but raised challenges. Public/private collaborations made major contributions to vastly accelerating new products, available worldwide, though unequally. Effective developments of living (i.e. regularly updated) reviews and guidelines, especially in Australia and Canada, extended existing expertise in meeting users' needs. Despite complexities, effective national policy responses (less evident in Brazil, the United Kingdom and the United States) also saved lives by drawing on health research system features, including collaboration among politicians, civil servants and researchers; good communications; and willingness to use evidence. Comprehensive health research strategies contributed to success in research production in the United Kingdom and in evidence use by political leadership in New Zealand. In addition to waste, challenges included equity issues, public involvement and non-COVID research. We developed recommendations, but advocate studies of further countries.
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 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 | 0,134 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».