98:oral How did European countries set priorities in response to the COVID-19 threat? A comparative document analysis of 24 pandemic preparedness plans
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Notice bibliographique
Résumé
<h3>Introduction</h3> The COVID-19 pandemic has forced governments across Europe to consider how to prioritise the allocation of scarce resources. Many took decisions to increase funding for health services, and to redirect current fiscal, human and technical resource towards meeting the new threat. <h3>Methods</h3> We conducted document analysis of pandemic preparedness plans in 24 countries across the regions of Europe, focussing on prioritisation and allocation of health-related resources. To be included, countries needed to have publicly available COVID19 preparedness plans. Where necessary, plans were translated into English before two members of the team conducted data extraction. We adapted the Kapiriri and Martin (2010) framework as our organising data extraction tool. Following validity checks, these data were synthesised numerically and thematically. <h3>Results</h3> COVID19 has engendered recognition on behalf of government of the scarcity of health care resources. However, many plans still fell short of identifying specific budgetary implications or trade-offs between COVID19 responses and other service priorities. Many plans describe use of evidence, expert involvement and decision making criteria. However, use of formal priority setting tools and frameworks was rare. The plans included very little engagement with citizens and service users, and equity considerations were often under-developed. The overall average compliance with quality parameters of priority setting was 29%. <h3>Discussion</h3> The plans indicate a political commitment to priority setting but underline the relative failure of priority setting methodologies to become embedded in governmental decision making processes. In the balance between ‘technocratic’ elements of priority setting and ‘processual’ dimensions, there was an emphasis on the former, reflecting the enforced speed with which plans were drawn up. As difficult priority setting decisions will be required in the post-crisis phase (as care backlogs and unmet need are addressed) it is likely that a rebalancing towards the processual aspects of decision making processes will be required.
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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 | 0,028 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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