Research priorities to reduce the impact of COVID-19 in low- and middle-income countries
Notice bibliographique
Résumé
Background: The COVID-19 pandemic has caused disruptions to the functioning of societies and their health systems. Prior to the pandemic, health systems in low- and middle-income countries (LMIC) were particularly stretched and vulnerable. The International Society of Global Health (ISoGH) sought to systematically identify priorities for health research that would have the potential to reduce the impact of the COVID-19 pandemic in LMICs. Methods: The Child Health and Nutrition Research Initiative (CHNRI) method was used to identify COVID-19-related research priorities. All ISoGH members were invited to participate. Seventy-nine experts in clinical, translational, and population research contributed 192 research questions for consideration. Fifty-two experts then scored those questions based on five pre-defined criteria that were selected for this exercise: 1) feasibility and answerability; 2) potential for burden reduction; 3) potential for a paradigm shift; 4) potential for translation and implementation; and 5) impact on equity. Results: Among the top 10 research priorities, research questions related to vaccination were prominent: health care system access barriers to equitable uptake of COVID-19 vaccination (ranked 1st), determinants of vaccine hesitancy (4th), development and evaluation of effective interventions to decrease vaccine hesitancy (5th), and vaccination impacts on vulnerable population/s (6th). Health care delivery questions also ranked highly, including: effective strategies to manage COVID-19 globally and in LMICs (2nd) and integrating health care for COVID-19 with other essential health services in LMICs (3rd). Additionally, the assessment of COVID-19 patients' needs in rural areas of LMICs was ranked 7th, and studying the leading socioeconomic determinants and consequences of the COVID-19 pandemic in LMICs using multi-faceted approaches was ranked 8th. The remaining questions in the top 10 were: clarifying paediatric case-fatality rates (CFR) in LMICs and identifying effective strategies for community engagement against COVID-19 in different LMIC contexts. Interpretation: Health policy and systems research to inform COVID-19 vaccine uptake and equitable access to care are urgently needed, especially for rural, vulnerable, and/or marginalised populations. This research should occur in parallel with studies that will identify approaches to minimise vaccine hesitancy and effectively integrate care for COVID-19 with other essential health services in LMICs. ISoGH calls on the funders of health research in LMICs to consider the urgency and priority of this research during the COVID-19 pandemic and support studies that could make a positive difference for the populations of LMICs.
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 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 | 0,159 | 0,146 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,012 | 0,007 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,008 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».