Mapping the Environmental Co-Benefits of Reducing Low-Value Care: A Scoping Review and Bibliometric Analysis
Bibliographic record
Abstract
Reducing low-value care (LVC) and improving healthcare's climate readiness are critical factors for improving the sustainability of health systems. Care practices that have been deemed low or no-value generate carbon emissions, waste and pollution without improving patient or population health. There is nascent, but growing, research and evaluation to inform practice change focused on the environmental co-benefits of reducing LVC. The objective of this study was to develop foundational knowledge of this field through a scoping review and bibliometric analysis. We searched four databases, Medline, Embase, Scopus and CINAHL, and followed established scoping review and bibliometric analysis methodology to collect and analyze the data. A total of 145 publications met the inclusion criteria and were published between 2013 and July 2023, with over 80% published since 2020. Empirical studies comprised 21%, while commentary or opinions comprised 51% of publications. The majority focused on healthcare generally (27%), laboratory testing (14%), and medications (14%). Empirical publications covered a broad range of environmental issues with general and practice-specific 'Greenhouse gas (GHG) emissions', 'waste management' and 'resource use' as most common topics. Reducing practice-specific 'GHG emissions' was the most commonly reported environmental outcome. The bibliometric analysis revealed nine international collaboration networks producing work on eight key healthcare areas. The nineteen 'top' authors were primarily from the US, Australia and Canada.
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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 | 0.048 | 0.232 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.210 | 0.215 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".