Nurses and Climate Change: A Narrative Review of Nursing Associations’ Recommendations for Integrating Climate Change Mitigation Strategies
Bibliographic record
Abstract
BACKGROUND: According to the World Health Organization, climate change is the greatest challenge of the twenty-first century. It is already affecting the health of many Canadians through extreme heat, wildfires and the expansion of zoonotic diseases. As trusted professionals, nurses are in favourable position to take action on climate change. PURPOSE: To document the recommendations issued by Quebec, Canadian, American and international nursing associations regarding nursing practices that address climate change or environmental issues. METHODS: This narrative review was conducted by establishing a list of environmental and general nursing associations in the geographical areas of interest through Google searches as well as by retrieving documents about climate change or environmental issues published by these organizations on their websites. Data related to the documents' characteristics and recommended nursing roles were then extracted. RESULTS: The review identified 13 nurses' organizations and 20 documents describing 37 recommendations for nurses in seven socioecological areas: individual, patient-focused, workplace, nursing associations, public health organizations, political and education. CONCLUSIONS: There is a gap between the breadth of roles that nurses may be called upon to play in addressing climate change and the degree to which relevant organizations are prepared to create the required conditions for them to do so. Several lessons emerged, including that the urgency of the climate crisis requires clear guidelines on how nurses can integrate climate change and its resultant health concerns into practice through nurses' associations, education and bottom-up nursing innovations. Funding is required for such initiatives, which must also prioritize health inequalities.
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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 | 0.020 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".