Investigating the recommendations and governmental actions to address the emerging risks of vector-borne diseases in Canada’s changing climate: A scoping review
Notice bibliographique
Résumé
Climate change is expected to increase the risks associated with vector-borne diseases, and its implications for human health are already observed across Canada. The objective of this review was to investigate the recommended adaptation strategies related to the risks associated with vector-borne diseases and examine how various levels of government in Canada are executing these recommended actions in their climate change adaptation plans. A combined methodology was employed, consisting of two distinct searches to examine both the recommended adaptation strategies in the peer-reviewed literature and the adaptation actions from governmental sources in the grey literature. Relevant sources were identified across four databases (Embase, Medline, Scopus, Global Health), as well as national, subnational, and municipal governmental websites across Canada. Data were categorized into eight (8) specific adaptation categories based on previously established frameworks. Data were also collected on which vector-borne diseases were referenced, the vulnerable population groups considered, and the inclusion of a One Health focus. A total of 194 peer-reviewed articles and 87 grey literature sources were reviewed, which contained a total of 582 adaptation recommendations and 178 adaptation actions. The most frequently proposed adaptation strategies related to the following categories: Management, Planning, and Policy, Information and Research, and Warning and Observation Systems. Our findings revealed a strong alignment between the recommended strategies and the adaptation measures being implemented. However, notable discrepancies were present among the adaptation categories of Practice and Behaviour and Laboratory Methods and Other Tools, revealing gaps across the literature and potential opportunities for further action. While many recommended strategies are being incorporated into actions across Canada, significant regional variability and gaps remain. We advocate for an increased investment in adaptation measures targeting vector-borne diseases and a greater integration of the One Health approach in subnational and municipal plans.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».