The scientific literature on Coronaviruses, COVID-19 and its associated safety-related research dimensions: A scientometric analysis and scoping review
Bibliographic record
Abstract
The COVID-19 global pandemic has generated an abundance of research quickly following the outbreak. Within only a few months, more than a thousand studies on this topic have already appeared in the scientific literature. In this short review, we analyse the bibliometric aspects of these studies on a macro level, as well as those addressing Coronaviruses in general. Furthermore, through a scoping analysis of the literature on COVID-19, we identify the main safety-related dimensions that these studies have thus far addressed. Our findings show that across various research domains, and apart from the medical and clinical aspects such as the safety of vaccines and treatments, issues related to patient transport safety, occupational safety of healthcare professionals, biosafety of laboratories and facilities, social safety, food safety, and particularly mental/psychological health and domestic safety have thus far attracted most attention of the scientific community in relation to the COVID-19 pandemic. Our analysis also uncovers various potentially significant safety problems caused by this global health emergency which currently have attracted only limited scientific focus but may warrant more attention. These include matters such as cyber safety, economic safety, and supply-chain safety. These findings highlight why, from an academic research perspective, a holistic interdisciplinary approach and a collective scientific effort is required to help understand and mitigate the various safety impacts of this crisis whose implications reach far beyond the bio-medical risks. Such holistic safety-scientific understanding of the COVID-19 crisis can furthermore be instrumental to be better prepared for a future pandemic.
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 | 0.042 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.200 | 0.211 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".