Nephrology Nurses: Essential Professionals in Sustainable Kidney Care
Bibliographic record
Abstract
Purpose: The increasing frequency of extreme climate events underscores the need for urgent action on climate change. The health care system contributes 4.6% of greenhouse gas emissions (GHGs) in Canada; thus, it is a major contributor to the country's carbon footprint. Kidney care in particular can involve high amounts of waste (eg, plastic and consumable waste associated with dialysis, transportation, emissions, energy, and water consumption). Therefore, sustainability initiatives within the health care system, and especially in the context of kidney care, have great potential to make a positive impact on planetary health. Here, we outline ways in which nephrology nurses can expand our duty of care to the environment and incorporate sustainability into our work. Sources of information: A small advisory group of nephrology nurses in partnership with the Canadian Association of Nurses for the Environment (CANE) assessed ways that sustainable practices can be incorporated into nephrology nursing. Drawing on the Planetary Health Care model used by the Canadian Society of Nephrology: Sustainable Nephrology Action Planning (SNAP) committee, we assessed how the model could be adapted in the context of kidney care using 3 main actionable themes in their work: reducing the demand for health services, matching the supply of health services with demand, and reducing emissions from the supply of health services. We also reviewed and selected real-world examples of initiatives pursued by colleagues. Key findings: Through this established framework, we provide recommendations and case examples for nephrology nurses to expand our duty of care to the environment. We describe nursing-led strategies used in Canada to improve environmental sustainability in kidney programs and consider their applicability to other renal programs. In 1 case example, we show how a simple nurse-led initiative at a single dialysis clinic can lower plastic waste and associated costs by $2042.59 per year. More broadly, we provide recommendations and actions for nephrology nurses to improve environmental sustainability in kidney care. Limitations: Nurses in Canada have many responsibilities within limited timeframes, making it essential to choose sustainable practices that do not exacerbate burnout and high workloads. For sustainable practices to be successful, nurses must integrate them into their existing workflows.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".