Metrics used in quality improvement publications addressing environmental sustainability in healthcare: A scoping review protocol
Bibliographic record
Abstract
Quality improvement approaches are increasingly being used to address the problem of healthcare's climate and ecological impact. While sustainability is increasingly recognized as a domain of quality, consensus is lacking on the most appropriate measures and metrics for those looking to reduce ecological impacts through quality improvement initiatives. We propose a scoping review to summarize approaches for selecting and quantifying ecological impacts in the published quality improvement literature. We will search multiple electronic databases (MEDLINE, EMBASE, CINAHL, and Scopus) from 2000 onwards, to identify published quality improvement initiatives in the human healthcare setting intended to address ecological impact with at least one quantitative measure of ecological impact, such as kilograms of carbon dioxide equivalent greenhouse gas. Two independent reviewers working in parallel will screen studies for inclusion and abstract study data, including publication, study, and ecological impact characteristics. Charted data will be synthesized narratively as well as with descriptive tables, figures, and summary statistics. In doing so, we will map areas of relative focus as well as gaps in the measurement of ecological impact across quality improvement initiatives. This map can in turn be used to raise awareness of ecological impacts requiring broader consideration, encouraging holistic and clinically relevant approaches to measuring ecological impact in future quality improvement work.
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 | 0.201 | 0.194 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.042 | 0.034 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".