Climate Change and Environmentally Sustainable Kidney Care in Canada: A Knowledge, Attitudes, and Practices Survey of Kidney Care Providers
Bibliographic record
Abstract
Background: Climate change impacts health and threatens the stability of care delivery systems, while healthcare is mobilizing to reduce its significant environmental impact. Objective: This study aimed to assess knowledge, attitudes, and practices (KAP) about climate change among Canadian kidney care providers. Design setting participants measurements and methods: An electronic KAP survey, created by the Canadian Society of Nephrology-Sustainable Nephrology Action Planning committee, was distributed to kidney care providers across Canada, from March to April 2023. Results: A total of 516 people responded to the survey. Most respondents (79%) identified as women; 83% were aged 30 to 59 years. Nurses and nephrologists made up 44% and 23% of respondents, respectively. About half of the participants felt informed about climate change to an average degree. Most respondents (71%; 349/495 and 62%; 300/489) were either extremely or very concerned about climate change and waste generated in their kidney care program, respectively. The vast majority of respondents (89%; 441/495) reported taking steps to lower their personal carbon footprint. People who felt more informed about climate change presented higher degrees of concern. Similarly, both those who felt more informed and those who reported higher degrees of concern about climate change were more likely to take steps to reduce their carbon footprint. Over 80% of respondents (314/386) were at least moderately interested in learning sessions about environmentally sustainable initiatives in care. Limitations: This survey is at risk of social acceptability, representative, and subjective bias. Overrepresentation from Quebec and British Columbia, as well as the majority of respondents identifying as women and working in academic centers, may affect generalizability of the findings. Conclusions: Most kidney care providers who responded to this survey are informed and concerned about climate change, and their knowledge is directly associated with attitude and practices. This indicates that educational initiatives to increase awareness and knowledge on climate change will likely lead to practice changes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".