Nurses’ perspectives on climate change, health and nursing practice
Bibliographic record
Abstract
AIMS AND OBJECTIVES: The purpose of this study was to explore Canadian nurses' perspectives on climate change, health, nursing practice and the relationships between these concepts. BACKGROUND: Climate change negatively impacts human health. With a mandate to promote health, nurses have a professional and ethical responsibility to address climate change. Little is known about Canadian nurses' perspectives on climate change or how they perceive of their professional responsibility towards addressing it. METHODS: A focused ethnography was conducted in three medicine units and the emergency room at a Canadian hospital. Nurses (n = 22) participated in semi-structured interviews, and observations were collected. Data were analysed via thematic analysis. Reporting is in accordance with the COREQ guideline. RESULTS: Three themes were identified: muddled terminology, climate change and health, and nursing's relationship to climate change. CONCLUSION: Participants had varying levels of knowledge about climate change and its relationship to health or practice. Climate change was a personal concern, and nursing's role in addressing it was not understood. RELEVANCE TO PRACTICE: This study highlighted that practising nurses did not readily recognise their role in addressing climate change. More work is needed to clarify this role and bring it into the consciousness of every-day nursing practice. Furthermore, more work is needed to examine how healthcare organisations can better support environmentally responsible nursing practice.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".