A bibliometric analysis of personal protective equipment and COVID-19 researches
Bibliographic record
Abstract
Background: COVID-19, which occurred at the end of December 2019, has evolved into a global public health threat. COVID-19’s high infectivity and mortality prompt governments and scientific community to respond quickly to the outbreak of the pandemic. The application of personal protective equipment (PPE) is of great significance in overcoming the epidemic situation. Although there were many studies about PPE and COVID-19, there is no study about bibliometric analysis of these studies. This study aims to provide a general overview of studies on PPE and COVID-19. Methods: On October 07, 2021, the Web of Science (WOS) Core Collection database was used to identify documents on PPE and COVID-19. HistCite and VOSviewer softwares were used for citation analysis and visualization mapping. Results: A total of 1462 documents authored by 6993 authors and published in 750 journals were included in the final analysis. The most prolific author was Macintyre CR. The USA was the most productive country with 463 published documents. The leading journal was Plos One. Network visualization map showed that USA was the largest international collaboration network. The keyword “COVID-19” had the strongest total link strengths (TLS) and largest number of occurrences. The New England Journal of Medicine was the leading source with highest TLS. The University of Toronto had the highest number of links and the highest TLS. Conclusions: The bibliometric analysis of PPE and COVID-19 provides an overall perspective, and the appreciation and study of these influential publications are very useful for future research.
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 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.211 | 0.271 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".