Transforming healthcare: the PEACH Approach to reducing emissions and achieving net-zero
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it