Recommendations for nurses and allied health professionals to help patients manage the cardio-renal impacts of climate change: findings from a systematic literature review
Bibliographic record
Abstract
Abstract Background Climate change is associated with more frequent extreme weather events that impact human health. Extreme heat and cold, air pollution and wildfire smoke all affect individuals’ cardio-renal health. There is not only an urgent need to mitigate climate change, but also a growing need for interventions to protect the health of patients. As trusted frontline workers, nurses and allied health professionals are well positioned to do both. Purpose To identify published data on effective interventions that nurses and allied health professionals can use to help their patients adapt to and protect against the impacts of climate change on cardio-renal health. Methods We performed a systematic literature review in PubMed and Embase for all publications reporting climate change and cardio-renal health-related interventions up to June 2024, using the terms community, individual, healthcare professional, intervention, climate change, and cardio-renal. Studies reporting preventive measures and/or adaptive strategies, observational studies, real-world studies, clinical studies, and case series with ≥10 cases were included. Results The searches identified 16,912 eligible records in total. After removing duplicate records, the titles/abstracts of 12,239 records were screened. The full text of 128 records were reviewed, and results of 18 articles were summarised (Figure 1). The studies highlighted effective interventions covering various cooling and rehydration strategies, reducing exposure to air pollution, and ways to deal with heat-related illness [1 - 14]. Two articles provided tips on how to approach the topic of mitigating climate change and adapting to its effects when interacting with patients [15, 16]. These included using ‘health-related’ rather than ‘climate-related’ language and using communication materials with graphics and concise language to explain how climate change affects health conditions. Another two publications mentioned the limited knowledge of the impacts of climate change on patient outcomes amongst health professionals [17, 18]. Conclusion Nurses and allied health professionals can promote simple measures to protect the health of patients, e.g. cooling strategies, rehydration strategies, and reducing exposure to air pollution. However, since not all health professionals are familiar with the health impacts of climate change, they may not necessarily recommend these interventions. Effective communication and education can empower patients and colleagues to protect patient and planetary health. We encourage nurses and allied health professionals to find ways to keep up to date on the exponentially growing information on the health impacts of climate change and consider this in their professional work.Figure 1:PRISMA flow diagram
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".