Attitudes and Perceptions of Canadian Otolaryngology‐Head and Neck Surgeons and Residents on Environmental Sustainability
Bibliographic record
Abstract
Objective: Healthcare systems, specifically operating rooms, significantly contribute to greenhouse gas emissions. Addressing operating room environmental sustainability requires understanding current practices, opinions, and barriers. This is the first study assessing the attitudes and perceptions of otolaryngologists on environmental sustainability. Study Design: Cross-sectional virtual survey. Setting: Email survey to active members of the Canadian Society of Otolaryngology-Head and Neck Surgery. Methods: A 23-question survey was developed in REDCap. The questions focused on four themes: (1) demographics, (2) attitudes and beliefs, (3) institutional practices, and (4) education. A combination of multiple choice, Likert-scale, and open-ended questions were employed. Results: Response rate was 11% (n = 80/699). Most respondents strongly believed in climate change (86%). Only 20% strongly agree that operating rooms contribute to the climate crisis. Most agree environmental sustainability is very important at home (62%) and in their community (64%), only 46% said it was very important in the operating room. Barriers to environmental sustainability were incentives (68%), hospital supports (60%), information/knowledge (59%), cost (58%), and time (50%). Of those involved in residency programs, 89% (n = 49/55) reported there was no education on environmental sustainability or they were unsure if there was. Conclusion: Canadian otolaryngologists strongly believe in climate change, but there is more ambivalence regarding operating rooms as a significant contributor. There is a need for further education and a systemic reduction of barriers to facilitate eco-action in otolaryngology operating rooms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".