Impact of COVID-19 on Indian biomedical research: A bibliometric analysis using online data from 2017 to 2022
Bibliographic record
Abstract
Objective. The global pandemic caused by the SARS-CoV-2 virus has led to a notable surge in research productivity across many academic disciplines, except research specifically focused on the virus. A significant increase in published research articles evidences this. In this study, we have analyzed the impact of the pandemic on recent publication trends in India by comparing data from three years before the pandemic with data from three years following the pandemic. Additionally, we have considered various factors that may have influenced this change. Methods. The Scopus database was searched for biomedical publications from January 1st, 2017, to December 31st, 2022, with India's country or territory specified as the limiting factor. The period was then divided into two parts: pre- and post-COVID-19. The pre-COVID-19 period spanned from 2017 to 2019, while the post-COVID-19 period spanned from 2020 to 2022. The publication trends in all subject areas across the periods above (pre- and post-COVID-19) were analyzed using appropriate nonparametric statistical tests and graphics. Results. In the specified period, India produced 231,370 research documents, which exceeded that of Australia (214,750) and France (207,220). However, it was lower than that of the top-performing publishing countries. The United States (148,448), China (71,484), the United Kingdom (42,446), Germany (31,727), Italy (7183), Japan (251,357), and Canada (241,759) also demonstrated notable research output. The discrepancy in research output between the pre-and post-pandemic periods was statistically significant (P < 0.001; Wilcoxon rank sum test, Z = 4.107). The publication output from the top institutions was significantly higher (P < 0.001, Wilcoxon signed-rank test, Z = 8.115). The statistically significant increase persisted in subgroup analysis for public and privately funded medical institutions, including medical colleges (P < 0.01). However, no significant difference in the rise in publication output pre- vs. post-COVID was observed when public institutions, private institutions, and medical colleges were mutually compared (P = 0.434, Kruskal-Wallis test). Conclusion. The global pandemic of the novel coronavirus (2019-nCoV) benefitted India's research output of biomedical disciplines. This effect was observed in public and privately funded medical institutions and academic centers. However, when the publication figures from these institutions were compared, no significant difference in the rise due to the 2019-nCoV pandemic was seen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.179 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.112 | 0.240 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".