Climate-resilient acute care clinical operations: A framework that informs how operations within acute care build climate-resilient health systems
Bibliographic record
Abstract
Increasingly, health leaders recognize climate change as a crucial issue for healthcare operations, requiring a whole-of-system approach to mobilizing governance, leadership, and resources to respond appropriately. Granular-level guidance is needed to guide the operationalization of adaptation, resiliency, and mitigation strategies specific to acute-care clinical operations within Canadian health facilities. We present the Climate-Resilient Acute Care Clinical Operations Framework to guide the development, implementation, and evaluation of strategies within clinical operations to build more climate-resilient acute care systems. The experience of Alberta Health Services, which is currently the largest provider responsible for the delivery of acute care within Alberta, is highlighted as a case study to demonstrate the practical adoption of this framework. As more health systems adopt similar strategies, sharing data and insights generated will contribute to ongoing iterations and adaptations, ensuring the framework evolves to meet the dynamic needs of healthcare sustainability.
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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 | 0.026 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.035 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".