A Gap Analysis to Assess the Implementation of Environmentally Sustainable Kidney Care Strategies in Canada
Bibliographic record
Abstract
Background: There is growing interest in the nephrology community for environmentally sustainable kidney care (ESKC) to alleviate the environmental impact of kidney care services. Objective: This study aimed to assess the knowledge of Canadian kidney care providers regarding their program's ESKC strategies. Design Setting Participants Measurements and Methods: An electronic survey, created by the Canadian Society of Nephrology-Sustainable Nephrology Action Planning committee, was distributed to Canadian kidney care providers. Results: A total of 421 Canadian kidney care providers responded to the survey. Various degrees of implementation of ESKC practices across the country were reported, with higher proportions of respondents reporting the use of strategies related to medication stewardship, clinical care consumables, virtual care options, office consumables, office equipment, and general waste management. It also highlighted the lack of knowledge of kidney care providers about many areas related to ESKC practices, such as energy sourcing, reverse osmosis reject water savings, procurement and product sourcing, as well as policies within the kidney program and contact with environmentally sustainable officers. Knowledge of respondents about certain strategies was also dependent on their role within the unit (eg, nephrologist vs nurse vs management), with nephrologists being relatively more aware of strategies that directly involve them, such as medication stewardship. Finally, variation across provinces was noted in terms of the incorporation of climate change adaptation or preparedness and environmental planning strategies. Limitations: The overrepresentation of people working in academic centers, as well as those from Quebec and British Columbia, may affect the generalizability of results. As respondents may be affiliated with the same units, results reflect knowledge of the individuals regarding the strategies, rather than the presence or implementation of such strategies across units. Conclusions: The ESKC practices from various domains are incorporated at different levels across the country, and there are important gaps in providers' awareness of such strategies, depending on their role within the unit.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".