Investigating the recommendations and governmental actions to address the emerging risks of vector-borne diseases in Canada’s changing climate: A scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.033 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".