Practicing nurses' and nursing students' perceptions of climate change: A scoping review
Bibliographic record
Abstract
BACKGROUND: Human activities have significantly contributed to a persistent climate change trend, posing substantial threats to human health. Nurses regularly interact with patients experiencing the consequences of climate change, making their engagement in addressing this issue crucial. Nonetheless, our understanding of nurses' viewpoints regarding climate change remains limited. AIM: This scoping review aims to identify practicing nurses' and nursing students' perceptions of climate change. DESIGN: To fulfil this objective, a documentary search strategy was developed using an iterative process. METHODS: The search strategy was tested in four bibliographic databases, as well as in the grey literature. A 2-stage selection process was conducted, and relevant data were extracted from selected articles for analysis. RESULTS: Twenty-two scientific articles and 11 documents from nursing associations were selected. The findings suggest that while many nurses and nursing students are concerned about climate change and its effects on their patients' health, their role in addressing the climate crisis is not well understood. Many barriers such as having a heavy workload and the lack of support hindered their ability to adjust their practice in response to the changing climate. Furthermore, many expressed a need for trainings on climate change issues. CONCLUSIONS: These results raise a great and urgent demand for these professionals to receive appropriate training to cope with climatic threats to health. Future research should focus on the development of nursing climate leadership, and healthcare organizations should support nursing initiatives and help raise nurses' awareness regarding climate change.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".