Bibliometric Evaluation of the Scopus Indexed Scholarly Literature of Ministry of National Guard – Health Affairs, Saudi Arabia
Bibliographic record
Abstract
The research analysis output is one of the leading indicators to assess the quality of clinical care, education, and research in healthcare organizations. This study aims to evaluate the scholarly publication growth of the Saudi Arabian Ministry of National Guard – Health Affairs (MNG-HA), indexed in the Elsevier’s Scopus database since 2002. The study was performed using different bibliometric and visualization techniques. While the highest number of publications indicate King Saud bin Abdulaziz University for Health Sciences as an affiliated address, however, the publications from King Abdulaziz Medical City have the maximum citation impact. The ‘Saudi Medical Journal’ has been the most preferred journal at national level, while ‘Studies in Health Technology and Informatics’ from the Netherlands at the international level. Our results show that most collaborations are among the authors of the United States, Canada, and the United Kingdom at the international level. The growing numbers of publications, sound citation-impact, and international collaboration reflect the practical approach of MNG-HA management’s leadership, and aspiring contribution of MNG-HA researchers.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.137 | 0.146 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".