Transforming healthcare: the PEACH Approach to reducing emissions and achieving net-zero
Bibliographic record
Abstract
The healthcare sector has recognised its significant emissions and climate impact, and is beginning to address emission hotspots. However, implementing necessary changes while working with current stressors in the sector such as high patient volumes, limited resources, and staffing shortages, remains a challenge. PEACH Health Ontario (Partnerships for Environmental Action by Communities within Health care systems) was launched in 2021 to address this and has grown to a national scope of work with some of our initiatives. This paper outlines the 'PEACH Approach' to guide healthcare towards a net-zero future. This article describes how PEACH Health Ontario and the PEACH Approach were developed. We identify the various areas of healthcare sustainability that PEACH focuses on as well as our approach to collaboration and engagement across the sector. The PEACH Approach has led to the creation of specialty-specific green guidebooks, the Green Office Toolkit, and other knowledge mobilisation materials targeting system-wide transformation. These solutions are developed through multidisciplinary collaboration and knowledge translation, ensuring practical and evidence-based recommendations. The PEACH Approach drives a cultural shift in healthcare sustainability, creating solutions that lead to tangible outcomes. By using knowledge translation, providing practical solutions, and engaging with stakeholders, PEACH charts a course forward for both people and the planet.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.037 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".