Taking action towards climate-resilient, low-carbon, health systems: Perspectives from Canadian health leaders and healthcare professionals
Bibliographic record
Abstract
Climate change poses significant public health and health system challenges including increased demand for health services due to chronic and acute health impacts from vector-borne diseases, heat-related illness, and injury from severe weather. As climate change worsens, so do its effects on health systems such as increasing severity of weather extremes causing damage to healthcare infrastructure and interference with supply chains. Ironically, health sectors globally are significant contributors to climate change, generating an estimated 5% of global emissions. Achieving "net zero" health systems require large-scale change with shared decision-making to coordinate a pan-Canadian approach to creating climate-resilient and low-carbon healthcare. In this article, we discuss healthcare professionals' and health leaders' perceptions of responsibility for practicing and advocating for climate-resilient and low-carbon healthcare in Canada.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.066 | 0.028 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".