Enhancing the sustainability and climate resiliency of health care facilities: a comparison of initiatives and toolkits.
Bibliographic record
Abstract
Extreme weather events have revealed the vulnerability of health care facilities and the extent of devastation to the community when they fail. With climate change anticipated to increase extreme weather and its impacts worldwide-severe droughts, floods, heat waves, and related vector-borne diseases-health care officials need to understand and address the vulnerabilities of their health care systems and take action to improve resiliency in ways that also meet sustainability goals. Generally, the health sector is among a country's largest consumers of energy and a significant source of greenhouse gas emissions. Now it has the opportunity lead climate mitigation, while reducing energy, water, and other costs. This Special Report summarizes several initiatives and compares three toolkits for implementing sustainability and resiliency measures for health care facilities: the Canadian Health Care Facility Climate Change Resiliency Toolkit, the U.S. Sustainable and Climate Resilient Health Care Facilities Toolkit, and the PAHO SMART Hospitals Toolkit of the World Health Organization/Pan American Health Organization. These tools and the lessons learned can provide a critical starting point for any health system in the Americas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".