Building climate-resilient and low-carbon healthcare systems in Canada: A need for policy shift for a path to net zero
Bibliographic record
Abstract
Climate change is straining Canada's health system. Canada pledged to develop climate-resilient and low-carbon sustainable health systems, with a net zero target. Despite this commitment, progress remains slow and fragmented, with many regions lacking cohesive, evidence-based strategies. While some provinces and health authorities have taken the lead, their efforts are hindered by inadequate investment. Limited data on low-carbon resilient strategies led to a comparative policy analysis of similar health systems to identify solutions. Canada can draw lessons from countries like the United Kingdom and Australia, which have committed to net zero health systems supported by robust national strategies. Australia's approach offers a model for Canada to follow, providing a clear governance structure, accountability mechanisms, and coordinated investments. A similar federal strategy could ensure alignment across provinces and drive transformative change. Without urgent action, Canada risks continued health sector emissions, further system deterioration, and rising health impacts, including preventable deaths.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".