Research Productivity at King Saud bin Abdul Aziz University for Health Sciences, Kingdom of Saudi Arabia: A Bibliometric Appraisal
Bibliographic record
Abstract
To examine the research outcome having an authorship affiliated with KSAU-HS, its teaching hospital and King Abdullah International Medical Research Centre (KAIMRC) since the inception of the university to December 2015. Method: Data retrieved from Institute of Scientific Information (ISI)Web of Science, InCiteTM Database of Thomson Reuters, which produced the list of 775 research documents published in 346 different journals. Some bibliometric indicators such as annual growth, subject segregation, authorship pattern, collaboration etc. had been used to illustrate the research performance of researchers. The data was analyzed by using SPSS 20. Results :Majority of articles (15.35%) were written on the subject of medicine, bulk (94%) of the research work had been carried out by collaborative efforts. In 475(61.29%) publications, the principal author belonged to KSAU-HS. Majority of the research work (64.65%) had been produced by the collaboration of other organizations.Research cooperation with the universities of United States was highest, followed by Canada and Pakistan. Conclusion: There is promising growth in biomedical publication and collaborating research trends are increasing.
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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 | 0.023 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.068 | 0.075 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".