Covid-19 and Emergency Medicine: A Scientometric Assessment of Global Publications
Bibliographic record
Abstract
This study analyses Covid-19 and Emergency Medicine research output during 2020-2021 on different parameters including global publications share, citation impact, contribution of authors and patterns of research communication in most productive and preferred journals. Web of Science Citation Database has been used to retrieve the data for 2 years (2020-2021) with 991 publications using the combined search of Covid-19 with topic field and Emergency Medicine with using Web of Science Subject Categories. The USA tops the list, with a publications share of 38.6%(383) followed far by Italy and UK ranks second with 7.7%(76), Canada with 7.6% and India ranks 14th positions with global publications share of 1.7 %(17)). The most productive Institutions are: Harvard Medical University lead with 34 Publications and received 134 Citations followed by University Toronto with 29 (96 Citations), Massachusetts Gen Hospital with 26 (86 Citations), Monash University with 22 (92 Citations), Columbia University and University Ottawa with 18 publications. The top most 5 preferred Journals are: AMERICAN JOURNAL OF EMERGENCY MEDICINE (IF: 1.70 with 141 publication followed far by ANNALS OF EMERGENCY MEDICINE (IF: 5.35) with 97 publications, RESUSCITATION (IF: 4.57) with 77, WESTERN JOURNAL OF EMERGENCY MEDICINE (IF: 1.80) with 71 and EMERGENCY MEDICINE JOURNAL (IF: 2.04) with 64. But its average annual publication growth rate and global publication share is high and Citation quality as reflected in Average Citations Per Paper is less. Concludes that the research needs to increase its output and bring about improvement in the quality of its research efforts. This can be done by investing much more international collaboration and by modernizing and strengthening its research infrastructure in the field of Medicines.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.103 | 0.131 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".