A climate resilience maturity matrix for Canadian health systems
Bibliographic record
Abstract
Healthcare decision-makers are becoming increasingly aware that climate change poses significant threats to population health and continued delivery of quality care. Challengingly, responding to climate change requires complex, often expensive, and multi-faceted actions to limit new emissions from worsening climate trajectories, while investing in climate-resilient systems. We present a Climate Resilience Maturity Matrix that brings together both mitigation and adaptation actions into a high-level tool for health leaders, for supporting organizational review, assessment, and decision-making for climate change readiness. This tool is designed to (i) support leaders in Canadian health facilities and regional health authorities in designing mitigation and adaptation roadmaps, (ii) support decision-making for climate change-related strategic planning processes, and (iii) create a high-level overview of organizational readiness. This tool is intended to consolidate key data, provide a clear communication tool, allow for objective rapid baselining, enable system-level gap analysis, facilitate comparability/transparency, and support rapid learning cycles.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".