Management of Used COVID-19 Personal Protective Equipment: A Bibliometric Analysis and Literature Review
Bibliographic record
Abstract
Using a science mapping approach, we analyzed the exponential increase in the number of scientific documents about the negative environmental impacts produced by waste from personal protective equipment (PPE), especially face masks, used to reduce SARS-CoV-2 transmission worldwide. Our results revealed that India, China, and Canada are leaders in this research field, which is clearly related to environmental issues, but also the solutions developed from an engineering point of view. Our analysis of the most-relevant documents in the field uncovered the considerable negative effects of PPE waste in aquatic media, its contribution to greenhouse gas emissions, effects on wildlife, etc. To reduce the negative environmental impacts of PPE waste, we need to implement innovative ecodesign strategies for their green production, including their re-use as and the use of recycling materials, but also a collaboration with the population to reduce PPE waste at its source. Both action lines could be materialized by establishing a collective, extended producer responsibility system for PPE to ensure their sustainable production and consumption. These well-implemented strategies will contribute to maintaining progress towards achieving sustainable development goals.
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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.148 | 0.159 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".