Improving Environmental Sustainability of Operating Theatres
Bibliographic record
Abstract
OBJECTIVE: To understand views of staff in relation to attitudes, enablers, and barriers to implementation of environmentally sustainable surgery in operating theatres. This will ultimately help in the goal of successfully implementing more sustainable theatres. BACKGROUND: Global health care sectors are responsible for 4.4% of greenhouse gas emissions. Surgical operating theatres are resource intensive areas and improvements will be important to meet Net-Zero carbon emissions within health care. METHODS: Three databases were searched (Web of Science, Ovid, and PubMed), last checked January 2024. We included original manuscripts evaluating staff views regarding sustainable operating theatres. The Mixed Methods Appraisal Tool was used for quality appraisal and data analysed using thematic synthesis. RESULTS: A total of 2933 articles were screened and 14 fulfilled inclusion criteria, using qualitative (1), quantitative (2), and mixed methods (11). Studies were undertaken in a variety of clinical (Department of Anaesthesia, Surgery, Otolaryngology, Obstetrics and Gynaecology and Ophthalmology) and geographical settings (Australia, Canada, France, Germany, New Zealand, United States, United Kingdom, and Ireland). Across studies there was a lack of evidence exploring enablers to implementation, but barriers mainly related to the following themes: education and awareness, leadership, resistance to change, facilities and equipment, time, and incentive. CONCLUSIONS: This systematic review identified attitudes and barriers perceived by clinicians towards improving environmental sustainability within operating theatres, which may inform future strategy towards sustainable surgery. Most studies used a survey-design, whereas use of interviews may provide deeper insights. Future work should be extended to wider stakeholders influencing operating theatres. In addition, implementation studies should be carried out to examine whether barriers do change in practice.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".