Healthcare procurement in the race to net-zero: Practical steps for healthcare leadership
Bibliographic record
Abstract
Although it is challenging to assess the greenhouse gas emission footprint associated with individual products and services, health leaders can play a pivotal role in emissions reduction by understanding and utilizing available tools and certifications that measure suppliers' operational environmental performance. Integrating environmental standards into procurement and supplier selection has the potential to greatly impact emissions production across the healthcare landscape as it will pressure suppliers to improve their operations in order to be selected. The purpose of this article is to emphasize the importance of the supply chain in addressing healthcare-related greenhouse gas emissions. We provide an overview of the types of tools available that can be used to evaluate the carbon footprints of individual companies and rate their performances, as well as certifications that formally recognize companies' sustainability practices and commitments.
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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 | 0.044 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".