Impact of COVID-19 on Indian biomedical research: A bibliometric analysis using online data from 2017 to 2022
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,112 | 0,240 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».