Global research trends at the intersection of coronavirus disease 2019 (COVID-19) and traditional, integrative, and complementary and alternative medicine: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a novel infectious disease caused by severe acute respiratory syndrome coronavirus 2, and responsible for a global pandemic. Despite there being no known vaccines or medicines that prevent or cure COVID-19, many traditional, integrative, complementary and alternative medicines (TICAMs) have been touted as the solution, as well as researched as a potential remedy globally. This study presents a bibliometric analysis of global research trends at the intersection of TICAM and COVID-19. METHODS: SCOPUS, MEDLINE, EMBASE, AMED and PSYCINFO databases were searched on July 5, 2020, with results being exported on the same day. All publication types were included, however, articles were only deemed eligible if they made mention of one or more TICAMs for the potential prevention, treatment, and/or management of COVID-19 or a health issue indirectly resulting from the COVID-19 pandemic. The following eligible article characteristics were extracted: title; author names, affiliations, and countries; DOI; publication language; publication type; publication year; journal (and whether it is TICAM-focused); 2019 impact factor, and TICAMs mentioned. RESULTS: A total of 296 eligible articles were published by 1373 unique authors at 977 affiliations across 56 countries. The most common countries associated with author affiliation included China, the United States, India and Italy. The vast majority of articles were published in English, followed by Chinese. Eligible articles were published across 157 journals, of which 33 were TICAM-focused; a total of 120 journals had a 2019 impact factor, which ranged from 0.17 to 60.392. A total of 327 TICAMs were mentioned across eligible articles, with the most common ones including: traditional Chinese medicine (n = 94), vitamin D (n = 67), melatonin (n = 16), phytochemicals (n = 12), and general herbal medicine (n = 11). CONCLUSIONS: This study provides researchers and clinicians with a greater knowledge of the characteristics of articles that been published globally at the intersection of COVID-19 and TICAM to date. At a time where safe and effective vaccines and medicines for the prevention and treatment of COVID-19 have yet to be discovered, this study provides a current snapshot of the quantity and characteristics of articles written at the intersection of TICAM therapies and COVID-19.
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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.201 | 0.282 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| 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".