Nursing students’ knowledge about climate change and its effect on health: A systematic review
Bibliographic record
Abstract
This systematic review aimed to investigate the extent of nursing students' knowledge regarding climate change and its impact on health. A comprehensive and systematic review was conducted across several international electronic databases, including Scopus, PubMed, and Web of Science. The search employed keywords based on Medical Subject Headings, including "knowledge", "climate change", "nursing students", and "health". The search period was extended from these databases’ inception until May 28, 2024. The quality of the studies included in this review was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS tool). The systematic review encompassed five cross-sectional studies, collectively involving 2,150 nursing students. Among these participants, females constituted 75.81%. The geographical distribution of the studies included in this systematic review spanned several countries: Egypt (n=3), Saudi Arabia, Iraq and Palestine (n=1), China (n=1), and the United States (n=1). Findings from three studies indicate that nursing students’ mean knowledge level regarding climate change’s impact on health is 63.70%. The knowledge level of nursing students regarding climate change’s impact on health was moderate. Factors including education level, practice, academic year, gender, and rural areas were related to nursing students’ knowledge about climate change and its effect on health. Policymakers and healthcare administrators must enhance the educational framework by prioritizing factors that influence the knowledge base of nursing students. These factors include academic level, geographic location (such as rural areas), gender, and clinical practice experience.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".