Health Care Facilities Resilient to Climate Change Impacts
Bibliographic record
Abstract
Climate change will increase the frequency and magnitude of extreme weather events and create risks that will impact health care facilities. Health care facilities will need to assess climate change risks and adopt adaptive management strategies to be resilient, but guidance tools are lacking. In this study, a toolkit was developed for health care facility officials to assess the resiliency of their facility to climate change impacts. A mixed methods approach was used to develop climate change resiliency indicators to inform the development of the toolkit. The toolkit consists of a checklist for officials who work in areas of emergency management, facilities management and health care services and supply chain management, a facilitator's guide for administering the checklist, and a resource guidebook to inform adaptation. Six health care facilities representing three provinces in Canada piloted the checklist. Senior level officials with expertise in the aforementioned areas were invited to review the checklist, provide feedback during qualitative interviews and review the final toolkit at a stakeholder workshop. The toolkit helps health care facility officials identify gaps in climate change preparedness, direct allocation of adaptation resources and inform strategic planning to increase resiliency to climate change.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".