Climate Change, Kidney Health, and Environmentally Sustainable Kidney Care
Bibliographic record
Abstract
Key Points A multinational survey of health care professionals on the kidney health impacts of climate change and the environmental burden of kidney care was conducted. Most participants reported knowledge gaps and high level of concern on these interconnected issues. Only a minority report personal or organizational initiatives in environmentally sustainable kidney care; this did not vary by country income level. Background Given the threat of climate change to kidney health and the significant environmental effect of kidney care, calls are increasing for health care professionals and organizations to champion climate advocacy and environmentally sustainable kidney care. Yet, little is known about their engagement, and existing literature is primarily emerging from high-income countries. Methods We conducted a cross-sectional survey to understand the knowledge, attitude, and practice of health care professionals on the interconnectedness of climate change and kidney health; to identify personal and organizational initiatives in sustainable kidney care and strategies to increase their engagement; and to compare responses by their country's income level as classified by the World Bank. Results Participants ( n =972) represented 108 countries, with 64% from lower- or middle-income countries. Ninety-eight percent believed that climate change is happening, yet <50% possessed knowledge about the effect of climate change on kidney health or the environmental effect of kidney care. Only 14% were involved in climate change and kidney health initiatives (membership, knowledge/awareness, research, and advocacy), 22% in sustainable kidney care initiatives (education/advocacy, preventative nephrology, sustainable dialysis, promoting transplant/home therapies, and research), and 26% reported organizational initiatives in sustainable kidney care (sustainable general or dialysis practices, preventative/lean nephrology, and focused committees). Participants from lower-income countries generally reported higher knowledge and variable level of concern. Engagement in sustainable kidney care did not vary by income level. Guidance/toolkit (79%), continuing education (75%), and opportunities (74%) were the top choices to increase engagement. National initiatives (47%), preventative measures (35%), and research endeavors (31%) were the top avenues for organizational engagement. These varied by income level, suggesting that the vision and priorities vary by baseline resource setting. Conclusions We have identified knowledge and practice gaps among health care professionals on the bidirectional relationship between kidney disease and climate change in a multinational context and several avenues to increase their engagement.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".