A Call to Action for Nurses in Canada to Address Climate-Driven Vector-Borne Diseases
Bibliographic record
Abstract
Purpose: Climate change is considered a public health emergency in Canada, and nurses must respond to health-related challenges faced by Canadians. The Canadian Association of Schools of Nursing (CASN) Guidelines for Undergraduate Nursing Education on Climate-Driven Vector-Borne Diseases provide a comprehensive framework to guide nurses to address these challenges. The purpose of this article is to situate the necessity of moving from knowledge to action within the five domains of the CASN guidelines to enhance nursing preparedness in education, research, and practice on planetary health and climate-related issues. Discussion: Strategies for moving from knowledge to action are presented within the domains of the CASN guidelines. Nurses’ expertise should include comprehensive knowledge of endemic and exotic vector-borne diseases (VBDs), risk communication, preventive and mitigation strategies, diagnostic and treatment practices, intersectoral collaboration, and advocacy approaches. Nurses in education and practice are required to take action to improve preparedness in addressing VBDs, and research on nurses’ practice readiness is recommended. Inclusion of a planetary health lens within the CASN Guidelines for Undergraduate Nursing Education on Climate-Driven Vector-Borne Diseases is recommended, to better align with the planetary health education framework. Conclusion: Nurses must be adequately prepared for future nursing practice and expanded roles within planetary health, which involves integrating climate change content into nursing curricula and updating nursing entry-to-practice competencies. Nursing education programs in Canada should consider the calls to action on planetary health and climate change and ensure this content is comprehensively integrated into nursing curricula, incorporating Indigenous Ways of Knowing and meaningful strategies for advocacy and leadership. It is imperative that nursing graduates are prepared for enhanced roles in leading change to advocate for a climate-resilient future.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".