Framework for Primary Prevention of Emerging Climate-Sensitive Diseases through the Lens of Planetary Health: The Case of Lyme Disease in Canada and Quebec
Bibliographic record
Abstract
This article contextualizes climate change and its impact on population health by highlighting the emergence of climate-sensitive infectious diseases. Using Lyme disease in Quebec as an example, it aims to achieve two objectives. First, the article demonstrates that the Quebec, and even Canadian, healthcare system is deficient regarding accessibility to treatment for this disease. Consequently, it emphasizes the need to optimize population health by proposing the enhancement of this pathology's preventive dimension by defining an intervention framework focused on planetary health. To this end, we defined a methodological approach based on a narrative review and the mobilization of some media sources. The results of this review suggest that the controversies surrounding diagnoses and treatments justify the inherent difficulties in accessing care. This, in turn, demonstrates the limitations of the healthcare system in Canada particularly in Quebec. The article highlights the necessity of strengthening primary prevention strategies by mobilizing planetary health as the main preventive framework. Thus, the results of the narrative review have allowed us to define preventive intervention strategies based on planetary health. These defined strategies form the proposed preventive framework to guide the primary prevention of climate-related diseases, particularly Lyme disease. These include ecosystem preservation, combating climate change, community education and awareness, adoption of preventive behaviors, sustainable agriculture and biodiversity, and environmental monitoring.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".