Implementation strategies for reducing carbon emissions in acute care: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to characterize evidence on implementation strategies to reduce carbon dioxide equivalent emissions in acute care settings. INTRODUCTION: Decarbonizing health care sectors is important for the sustainability of health systems and mitigating greenhouse gas emissions. While evidence on the environmental impacts of health care is growing, there is limited understanding of how interventions to reduce emissions are implemented and what strategies support health care adaptation to reduce emissions. ELIGIBILITY CRITERIA: Articles published in English since 1999 will be included if they report on interventions or implementation strategies to reduce carbon dioxide equivalent emissions in acute care settings. Eligible studies may use quantitative, qualitative, or mixed methods and involve any health care professional, staff, clinical specialty, or activity (eg, recycling, anesthesia, prescribing). Studies conducted outside acute care or lacking information related to reducing carbon dioxide equivalent emissions will be excluded. METHODS: This review will follow JBI scoping review methodology. MEDLINE (Ovid), Embase (Elsevier), Scopus, and CINAHL (EBSCOhost) will be searched for peer-reviewed articles on emissions reduction in health care. Data will be extracted, synthesized, and categorized using the Expert Recommendations for Implementing Change (ERIC) taxonomy of implementation strategies and the behavior change technique taxonomy. Results will be presented in tables, creating an inventory of intervention types and implementation strategies for reducing emissions in health care. This review will provide a comprehensive overview of strategies for reducing carbon dioxide equivalent emissions in acute care, contributing to efforts to decarbonize health care systems and support climate change mitigation. REVIEW REGISTRATION: OSF https://osf.io/e8d4r.
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 | 0.101 | 0.081 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.016 |
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".