A Systematic Review of Studies on Public Health Status during the COVID-19 Pandemic by Visualizing their Structure using Co-Occurrence and VOSviewer Software
Bibliographic record
Abstract
Abstract Background Since the outbreak of the COVID-19 epidemic, many public health issues have arisen. However, based on bibliometric analyses, little attention has been paid to overall trends in this area. This study sought to describe the status of public health events in the COVID-19 pandemic using systematic review of high-quality research evidence, thereby revealing the trend of public health during COVID-19, research hotspots, and provide lessons and references for future research in the field of public health emergencies. Methods Co-Occurrence and VOSviewer bibliometric methods were used to analyze the literature related to the public health during COVID-19 in the Web of Science (WOS) core database. The public health characteristics during the COVID-19 pandemic were explored by analyzing the number of publications, countries, institutions, and keywords. Results This study included 1911 original research articles and reviews in English on public health issues during the pandemic. The United States, China, and England are the main forces in this field, and they collaborate closely with each other. Research institutions in each country are dominated by universities, with the University of Toronto being the most productive institution in the world. The authors with most publications are Larson, Looi, and Neumark-sztainer. Visual analysis shows that the main focus of research are characteristics of the COVID-19 pandemic, problems encountered by Citizens during the COVID-19 pandemic and responses to Public Health issues during the COVID-19 pandemic. Conclusions These results reveal emerging research on public health issues during the COVID-19 pandemic, especially the impacts caused by the pandemic on public health determinants.
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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 | 0.018 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.105 | 0.091 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".