Nurses’ perceptions of climate sensitive vector‐borne diseases: A scoping review
Bibliographic record
Abstract
OBJECTIVE: Nurses are well positioned to play an integral role in the mitigation of climate change and climate-driven vector-borne diseases, however, they lack awareness and knowledge about their role. The purpose of this scoping review was to map existing literature on nurses' perceptions, knowledge, attitudes, and experiences with vector-borne diseases, specifically Lyme disease and West Nile virus. DESIGN: A scoping review was conducted using Joanna Briggs Institute (JBI) scoping review methodology. CINAHL, ProQuest Nursing & Allied Health Premium, MEDLINE, APA PsycINFO, ProQuest Dissertations & Theses, and Web of Science were searched for English-language publications. The PRISMA-ScR was used. After initial screening as per study protocol, a total of 33 items were reviewed independently by four reviewers. RESULTS: Thirty-three articles, including seven sources from grey literature, met the criteria for this scoping review. Results were mapped according to the five domains of the Guidelines for Undergraduate Nursing Education on Climate-Driven Vector-Borne Diseases. CONCLUSIONS: Findings from the review indicate that nurses play a role in climate-related health effects and should be knowledgeable about vector-borne diseases. However, scant literature exists on nurses' knowledge, perceptions, attitudes toward vector-borne diseases, and practice readiness, signifying a need for further research on this emerging topic.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".