Sustainability in Health Care
Bibliographic record
Abstract
The academic public health and biomedical communities have a long history of researching and documenting the adverse impacts of pollution on human health. However, the healthcare industry itself is a major contributor to pollution as well as the greenhouse gas (GHG) emissions responsible for global warming. For example, the health sectors of the United States, Australia, England, and Canada are estimated to emit a combined 748 million metric tons of carbon dioxide equivalents annually, equivalent to a nation that would rank seventh in the world for GHG emissions. Moreover, the healthcare sector is a major consumer of natural resources, thereby contributing to the imbalances characteristic of what is increasingly being referred to as the Anthropocene and a threat to planetary health. In this article, we summarize current information on the healthcare industry's environmental footprint and the potential for markedly reducing that footprint by applying the principles and tools of sustainability science. We discuss some of the industry's special challenges, including those associated with new construction (which have undergone relatively little examination in relation to sustainability, despite predictions of accelerated growth). We examine current ideas and efforts to advance sustainability solutions in the healthcare industry, in high-, middle-, and low-income countries alike, where the healthcare industry can be expected to grow the fastest. Finally, we review case studies and discuss research needs.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".