How Can Non-Hospital Surgical Centres Improve Their Environmental Footprint (and Reduce Costs)?
Bibliographic record
Abstract
Introduction: Every industry has greenhouse gas emissions, with healthcare a significant contributor. In Canada, the healthcare sector is directly and indirectly responsible for 4.6% of the country's greenhouse gas emissions. Operating rooms (ORs) are major contributors to hospital waste, making the OR low hanging fruit for analyzing environmental practices. The OR can adopt a green mindset to reduce its carbon footprint, yet barriers to going green exist. Herein we study non-hospital surgical centres in British Columbia to assess current green practices, attitudes towards environmental sustainability, and barriers to implementation. Methods: All accredited non-hospital surgical centres in BC were invited to complete a survey on current practices and plans to reduce their environmental impact. Results: Of 56 non-hospital surgical centres contacted, 18 responded, with 89% willing to adapt their practice to promote environmental sustainability, yet lacked current knowledge (56%) and formal plans (0%). The wide use of anesthetic gases with high global warming potential (64%) and disposable drapes/ gowns (78%/ 67%) were noted. Barriers to adopting green practices included: cost (44%), infrastructure (44%), regulatory guidelines (39%), knowledge (39%), and safety (28%). Conclusions: Transitioning to more environmentally sustainable practices in ORs can enhance healthcare value by reducing both costs and greenhouse gas emissions. The greatest effect can be achieved through prudent choice of anesthetic gas agent, followed by reusable linens and drapes. Education and regulatory leadership were identified as crucial for overcoming these barriers. This study underscores the need for education, guidelines, and economically viable options to transition from awareness to action.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".