Exploring the Knowledge, Attitudes, and Perceptions of Hospital Staff and Patients on Environmental Sustainability in the Operating Room: Quality Improvement Survey Study
Bibliographic record
Abstract
BACKGROUND: In Canada, the health care system has been estimated to generate 33 million metric tons of greenhouse gas emissions annually. Health care systems, specifically operating rooms (ORs), are significant contributors to greenhouse gas emissions, using 3 to 6 times more energy than the hospital's average unit. OBJECTIVE: This quality improvement study aimed to investigate the knowledge, attitudes, and perceptions of staff members and patients on sustainability in the OR, as well as identify opportunities for initiatives and barriers to implementation. METHODS: A total of 2 surveys were developed, consisting of 27 questions for staff members and 22 questions for patients and caregivers. Topics included demographics, knowledge and attitudes regarding environmental sustainability, opportunities for initiatives, and perceived barriers. Multiple-choice, Likert-scale, and open-ended questions were used. RESULTS: A total of 174 staff members and 37 patients participated. The majority (152/174, 88%) of staff members had received no and minimal training on sustainability, while 93% (162/174) cited practicing sustainability at work as moderately to extremely important. Among patients and caregivers, 54% (20/37) often or always noticed when a hospital is being eco-friendly. Both staff members and patients agreed that improving sustainability would boost satisfaction (125/174, 71.8% and 22/37, 59.4%, respectively) and hospital reputation (22/37, 59.4% and 25/37, 69.5%, respectively). The staff members' highest-rated environmental initiatives included transitioning to reusables, education, and improved energy consumption, while patients prioritized increased nature, improved food sourcing, and education. Perceived barriers to these initiatives included cost, lack of education, and lack of incentives. CONCLUSIONS: Staff members and patients and caregivers in a large academic health care center acknowledge the significance of environmental sustainability in the OR. While they do not perceive a direct impact on patient care, they anticipate positive effects on satisfaction and hospital reputation. Aligning initiatives with staff members and patient and caregiver preferences can help drive meaningful change within the OR and beyond.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".