Evaluating progress and accountability for achieving COP26 Health Programme international ambitions for sustainable, low-carbon, resilient health-care systems
Bibliographic record
Abstract
A global initiative to develop low-carbon, resilient health systems-the COP26 Health Programme-launched at the UN Framework Convention on Climate Change 26th Conference of the Parties (COP26) in 2021. As of May, 2024, 83 nations have committed to participate in this initiative. This analysis evaluates the effectiveness of existing and proposed indicators towards public monitoring and accountability to these commitments. Our findings reveal substantial gaps in data availability and indicator relevance, with many countries reporting process indicators that do not reflect actual progress towards achieving sustainable health-care systems. We found a dearth of suitable indicators and an urgent need to develop robust ones that are adaptable to different health-care system contexts. These indicators should be designed to capture tangible outcomes, support policy making, and prevent greenwashing. Integration of more robust indicators into independent scientific monitoring can support systematic inclusion of health care in global climate strategies, thereby enhancing the overall effectiveness of the COP26 Health Programme.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".