Empowering health-care learners to take action towards embedding environmental sustainability into health-care systems
Bibliographic record
Abstract
Health care is one of the most important sectors in addressing the growing health impacts of climate change, but it has also been identified as a significant contributor to the climate crisis, being responsible for 4·6% of global greenhouse gas emissions in 2017.1 The negative environmental effects of health care are growing, with greenhouse gases and particulate matter emissions from health-care systems increasing by 29% and 9%, respectively, between 2000 and 2015.2 There is thus a tremendous impetus to reduce the environmental impact of health-care delivery and achieve net-zero health-care services.
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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".