Embedding Climate Literacy in Canadian Nursing Curricula and Research: Lessons from Wildfires and Heat Waves
Bibliographic record
Abstract
Climate change is one of the greatest global health challenges of the twenty-first century, with wildfires, heat waves, floods, and other extreme events posing profound threats to health systems, communities, and vulnerable populations. Nurses, as the largest segment of the healthcare workforce, are uniquely positioned to respond to these crises, yet climate literacy and climate-health research remain underdeveloped in nursing. While recent progress has been made in embedding environmental health into Canadian nursing curricula, implementation is inconsistent, and research examining the intersections of climate change, health outcomes, and nursing practice is limited. This editorial argues that advancing both nursing education and nursing research is essential to prepare the profession for the realities of a climate-altered world. Climate literacy must be integrated into all levels of nursing education, moving beyond elective or peripheral status to become a core competency. At the same time, nursing research must expand its scope to evaluate disaster nursing interventions, address inequities faced by Indigenous and racialized communities, explore community resilience strategies, and assess the long-term impacts of climate-focused education on workforce readiness. By embedding climate literacy in curricula and prioritizing nursing research, the discipline can generate evidence to inform practice, shape policy, and strengthen health system resilience. Nurses equipped with climate literacy competencies will be able to provide effective care during climate-related disasters, advocate for systemic reforms, and build equitable, sustainable communities. In doing so, nursing can take a leadership role in addressing the health impacts of climate change and advancing global health equity.
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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 | 0.033 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".