Global Trends in Nursing-Related Research on COVID-19: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) has significantly impacted healthcare, especially the nursing field. This study aims to explore the current status and hot topics of nursing-related research on COVID-19 using bibliometric analysis. Methods: Between 2019 and 2022, publications regarding nursing and COVID-19 were retrieved from the Web of Science core collection. We conducted an advanced search using the following search query string: TS = ("Novel coronavirus 2019" or "Coronavirus disease 2019" or "COVID 19" or "2019-nCOV" or "SARS-CoV-2" or "coronavirus-2") and TS = ("nursing" or "nurse" or "nursing-care" or "midwife"). Bibliometric parameters were extracted, and Microsoft Excel 2010 and VOSviewer were utilized to identify the largest contributors, including prolific authors, institutions, countries, and journals. VOSviewer and CiteSpace were used to analyze the knowledge network, collaborative maps, hotspots, and trends in this field. Results: A total of 5,267 papers were published between 2020 and 2022. The findings are as follows: the USA, China, and the UK are the top three prolific countries; the University of Toronto, the Harvard Medical School, the Johns Hopkins University, and the Huazhong University of Science & Technology are the top four most productive institutions; Gravenstein, Stefan, and White, Elizabeth M. from Brown University (USA) are the most prolific authors; The International Journal of Environmental Research and Public Health is the most productive journal; "COVID-19," "SARS-CoV-2," "nurse," "mental health," "nursing home," "nursing education," "telemedicine," "vaccine-related issues" are the central topics in the past 2 years. Conclusion: Nursing-related research on COVID-19 has gained considerable attention worldwide. In 2020, the major hot topics included "SARS-CoV-2," "knowledge," "information teaching," "mental health," "psychological problems," and "nursing home." In 2021 and 2022, researchers were also interested in topics such as "nursing students," "telemedicine," and "vaccine-related issues," which require further investigation.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.232 | 0.285 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".