Bibliometric analysis of the International Medical Informatics Association official journals
Bibliographic record
Abstract
Objectives: This research article aims to analyze the bibliometric characteristics of four official International Medical Informatics Association (IMIA) journals, namely: the International Journal of Medical Informatics, Methods of Information in Medicine, Applied Clinical Informatics, and Informatics for Health and Social Care.Method: We used descriptive bibliometrics to study the trends of literature production, identify documents` types, most prolific authors, institutions, countries, and most cited publications of all four IMIA journals. Additionally, we visualized the content of published publications using bibliometric mapping to identify journals’ main themes and the most prolific and most cited research terms.Results: In total, 6,837 publications were published in all four IMIA journals. Among them, there were 5,137 original articles, meaning that articles were the leading document type. Research is being conducted globally among various research institutions. The most prolific countries are the United States of America, the United Kingdom, Germany, the Netherlands, and Canada. Thematic analyses of clusters show that themes are overlapping between all four journals.Conclusion: The journals contribute to the advances in technology related to health information systems, knowledge-based and decision-making systems, health literacy, and electronic health records.
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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 | 0.013 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.163 | 0.220 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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, 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".