Implementation approaches to improve environmental sustainability in operating theatres: a systematic review
Bibliographic record
Abstract
Operating theatres consume large amounts of energy and consumables and produce large amounts of waste. There is an increasing evidence base for reducing the climate impacts of healthcare that could be enacted into routine practice; yet, healthcare-associated emissions increase annually. Implementation science aims to improve the systematic uptake of evidence-based care into practice and could, therefore, assist in addressing the environmental impacts of healthcare. The aim of this systematic search with narrative synthesis was to explore what implementation approaches have been applied to reduce the environmental impact of operating theatre activities, described by implementation phases and methodologies. A search was conducted in EMBASE, PubMed, and CINAHL, limited to English and publication since 2010. In total, 3886 articles were retrieved and 11 were included. All were in the exploratory phase (seven of 11) or initial implementation phase (four of 11), but none were in the installation or full implementation phase. Three studies utilised a recognised implementation theory, model, or framework in the design. Four studies used interprofessional education to influence individuals' behaviour to reduce waste, improve waste segregation, or reduce anaesthetic gases. Of those that utilised behaviour change interventions, all were qualitatively successful in achieving environmental improvement. There was an absence of evidence for sustained effects in the intervention studies and little follow-up from studies that explored barriers to innovation. This review demonstrates a gap between evidence for reducing environmental impacts and uptake of proposed practice changes to deliver low-carbon healthcare. Future research into 'greening' healthcare should use implementation research methods to establish a solid implementation evidence base. SYSTEMATIC REVIEW PROTOCOL: PROSPERO CRD42022342786.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".