Establishing research priorities in prevention and control of vector-borne diseases in urban areas: a collaborative process
Notice bibliographique
Résumé
BACKGROUND: In 2015, following a call for proposals from the Special Programme for Research and Training in Tropical Diseases (TDR), six scoping reviews on the prevention and control of vector-borne diseases in urban areas were conducted. Those reviews provided a clear picture of the available knowledge and highlighted knowledge gaps, as well as needs and opportunities for future research. Based on the research findings of the scoping reviews, a concept mapping exercise was undertaken to produce a list of priority research needs to be addressed. METHODS: Members of the six research teams responsible for the "VEctor boRne DiseAses Scoping reviews" (VERDAS) consortium's scoping reviews met for 2 days with decision-makers from Colombia, Brazil, Peru, Pan-American Health Organization, and World Health Organization. A total of 11 researchers and seven decision-makers (from ministries of health, city and regional vector control departments, and vector control programs) completed the concept mapping, answering the question: "In view of the knowledge synthesis and your own expertise, what do we still need to know about vector-borne diseases and other infectious diseases of poverty in urban areas?" Participants rated each statement on two scales from 1 to 5, one relative to 'priority' and the other to 'policy relevance', and grouped statements into clusters based on their own individual criteria and expertise. RESULTS: The final map consisted of 12 clusters. Participants considered those entitled "Equity", "Technology", and "Surveillance" to have the highest priority. The cluster considered the most important concerns equity issues, confirming that these issues are rarely addressed in research on vector-borne diseases. On the other hand, the "Population mobility" and "Collaboration" clusters were considered to be the lowest priority but remained identified by participants as research priorities. The average policy relevance scores for each of the 12 clusters were roughly the same as the priority scores for all clusters. Some issues were not addressed during the brain-storming. This is the case for governance and for access and quality of care. CONCLUSIONS: Based on this work, and adopting a participatory approach, the concept mapping exercise conducted collaboratively with researchers from these teams and high-level decision-makers identified research themes for which studies should be carried out as a priority.
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 | 0,000 | 0,003 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 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 ».