A Cross-Sectional Study on Indigenous Nurses’ Knowledge and Perceptions toward Planetary Health Challenges
Bibliographic record
Abstract
Background: Planetary health challenges—such as climate change and vector-borne diseases—not only threaten human health, but also jeopardize food and water security, ecosystems, economic stability, and social well-being. Registered nurses play an integral role in supporting populations affected by planetary health challenges. Purpose: The purpose of the larger cross-sectional study was to investigate the knowledge, attitudes, and practices of registered nurses in Canada related to climate sensitive vector-borne diseases. This manuscript presents findings of the Indigenous participants from the larger study. Methods: A national self-administered digital survey was distributed to practicing registered nurses in Canada. Results: Of the 382 survey respondents, 35 respondents declared as Indigenous, Metis, or Inuit. Results indicated that most worked as frontline care providers, and several were nurse educators. Study findings revealed enhanced knowledge of climate change and vector-borne diseases, as well as increased awareness of, confidence toward, preparedness, and experiences with vector-borne diseases in practice demonstrated by Indigenous, Metis, and Inuit participants. The greater knowledge and confidence of Indigenous, Metis, and Inuit nurses toward climate change and vector-borne diseases may be attributed to intergenerational knowledge transfer, which has provided them with the knowledge to observe and adapt to climate-related concerns, such as the changing vector landscape. Conclusion: Indigenous nurses are well-positioned to lead the nursing profession to a decolonization of nursing knowledge, where Indigenous knowledge is used to educate and prepare nurses to address planetary challenges 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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".