The evolution of research on depression during COVID-19: A visual analysis using Co-Occurrence and VOSviewer
Bibliographic record
Abstract
Background: The COVID-19 pandemic has led to public health problems, including depression. There has been a significant increase in research on depression during the COVID-19 pandemic. However, little attention has been paid to the overall trend in this field based on bibliometric analyses. Methods: Co-Occurrence (COOC) and VOSviewer bibliometric methods were utilized to analyze depression in COVID-19 literature in the core collection of the Web of Science (WOS). The overall characteristics of depression during COVID-19 were summarized by analyzing the number of published studies, keywords, institutions, and countries. Results: A total of 9,694 English original research articles and reviews on depression during COVID-19 were included in this study. The United States, China, and the United Kingdom were the countries with the largest number of publications and had close cooperation with each other. Research institutions in each country were dominated by universities, with the University of Toronto being the most productive institution in the world. The most frequently published author was Ligang Zhang. Visualization analysis showed that influencing factors, adverse effects, and coping strategies were hotspots for research. Conclusion: The results shed light on the burgeoning research on depression during COVID-19, particularly the relationship between depression and public health. In addition, future research on depression during COVID-19 should focus more on special groups and those at potential risk of depression in the general population, use more quantitative and qualitative studies combined with more attention to scale updates, and conduct longitudinal follow-ups of the outcomes of interventions. In conclusion, this study contributes to a more comprehensive view of the development of depression during COVID-19 and suggests a theoretical basis for future research on public health.
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 | 0.013 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.165 | 0.136 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".