Registered Nurses' Knowledge, Attitudes, and Practices Toward Climate‐Sensitive Vector‐Borne Diseases: Findings From a Cross‐Sectional Survey
Bibliographic record
Abstract
OBJECTIVE: Climate change is contributing to increasing rates of vector-borne diseases, affecting global population health. As the largest group of regulated health professionals, nurses play an integral role in climate-related health challenges. The purpose of this research study was to investigate the knowledge, attitudes, and practices of registered nurses in Canada related to climate sensitive vector-borne diseases. DESIGN: Cross-sectional survey. SAMPLE: A national online survey was distributed to practicing registered nurses, through contact with nursing organizations and regulatory bodies, as well as social media. MEASUREMENTS: Three hundred and eighty-two survey responses were included in data analysis. RESULTS: Research findings suggest that nurses' knowledge on climate change and vector-borne diseases was limited, especially among frontline nurses and those in Western and Northern regions of Canada. There was greater knowledge of Lyme disease compared to West Nile virus, particularly among nurses working in endemic areas. Participants did not often consider vector-borne diseases in practice and demonstrated a lack of confidence and preparedness in addressing in practice. CONCLUSIONS: The study validates that while climate-related issues are important for nurses, nurses must be better prepared to address vector-borne diseases in practice and assume a greater role in leading change to advocate for a climate-resilient future.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".