The Lancet Commission on Sustainable Health Care measurement framework for advancing sustainable health care transformation
Bibliographic record
Abstract
As health-care systems and organisations worldwide transition to sustainable health care, reliable guidance and standardised approaches are needed to monitor and report progress. A robust measurement framework can inform the development of indicators to track progress, compare performance, guide interventions, and reduce the risk of greenwashing. The Lancet Commission on Sustainable Health Care convened a working group to develop a measurement framework to support data-driven and evidence-based indicators in comprehensively assessing health-care system performance across environmental and health outcomes dimensions. The working group included representatives from several disciplines, such as environmental engineering, industrial and social ecology, health promotion theory, environmental chemistry, sustainability, health-care quality and safety, clinical care, epidemiology, and public policy, and diverse geographical settings. The measurement framework developed by the Lancet Commission on Sustainable Health Care integrates concepts from previous frameworks and approaches and encompasses sustainability concepts across two domains: the physical domain (contributing directly and indirectly to resource use, operational resilience, emissions, and environmental impacts) and the people, policies, and programmes domain (contextual characteristics surrounding operations in nations and organisations). Each domain is divided into five categories: inputs, structures, processes, outputs, and outcomes or effects, including health effects. In this Personal View, we describe the conceptual development of this measurement framework; the indicators for performance measurement by health-care organisations and countries will be presented in companion papers. The framework aims to address all three aspects of performance measurement-namely, research, improvement of health-care system performance, and accountability to external entities. The proposed measurement framework can guide the development and implementation of indicators for health-care system benchmarking and monitoring, aiming to accelerate the global advancement of sustainability-related health-care performance by adopting evidence-based policies and practices.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".