Worldwide scientific efforts on nursing in the field of SARS-CoV-2: a cross-sectional survey analysis
Bibliographic record
Abstract
INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been a global public health issue. This study aimed to characterize global nursing research on SARS-CoV-2. METHODOLOGY: Nursing-related publications through December 31, 2022, were identified using Scopus. The number of studies, study types, countries, institutions, journals, authors, h-index, total confirmed cases, total deaths, and the highest-cited studies were investigated. RESULTS: In total, 12,427 studies were identified. The number of studies increased rapidly, particularly between 2020 and 2021, with a 2.36-fold increase. The United States published the most studies (3,289, 26.47%), followed by the United Kingdom (1,059, 8.52%) and China (877, 7.06%). Scientific productivity significantly correlated with the total confirmed cases (r = 0.701, p = 0.024) and total deaths (r = 0.804, p = 0.005). The United States had the highest h-index (80), followed by China (59), and the United Kingdom (57). The University of Toronto published the most studies (181), followed by Harvard Medical School (165), and the University of São Paulo (107). Gravenstein S (23) was the most prolific author, followed by Mor V (22), and Rosa WE (19). The International Journal of Environmental Research and Public Health published the most papers (436), followed by PLOS ONE (219), and BMJ Open (185). CONCLUSIONS: Several countries, institutions, journals, and authors contributed greatly to SARS-CoV-2-related nursing studies. Countries with larger numbers of confirmed cases and deaths tended to publish more nursing studies. The United States, United Kingdom, and China had the highest quantity and quality of studies.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".