SDG13, climate action: health systems as stakeholders and implementors in climate policy change
Bibliographic record
Abstract
Climate Action is one of the United Nation’s Sustainable Development Goals. Yet, despite calls for action, global governments have broadly not taken consequential change to reduce carbon outputs and mitigate warming. Our chapter argues that a primary cause of this inaction is political conflict and policy capacity. Without strong economic incentives and facing constrained resources, governments may opt to proceed with the status quo. Here, health systems present a critical resource to engage nations in climate action. Health systems produce political leverage as major political stakeholders across nations, globally, for engaging in broader climate policy and a wealth of resources inherent to health systems – expertise, funding – to directly implement climate policy. The case study of the city of Toronto in Canada offers lessons for directly involving health systems in subnational climate action as policy stakeholders and implementors, and the co-benefits health system engagement brings to promote climate action intersectorally. Toronto provides an important case for high-latitude countries that will soon be facing climate hazards tropical nations have been grappling with for centuries. Engaging health systems in climate action policy processes may improve the likelihood of success for strengthening resilience and adaptivity to climate related hazards.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".