Medical research productivity in the Arab countries: 2007-2016 bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to assess recent trends in medical research productivity in Arab countries. METHODS: We collected bibliometric data for the world countries, Arab countries, and Arab institutions for 2007-2016, using Essential Science Indicators, Journal Citation Reports, and Web of Science database. We collected the number of published papers overall and per year, citations per paper, and number of papers published in top quartile and top 10% journals. For the 10 most productive institutions, we additionally collected the number of papers with correspondence authors affiliated with the institution. RESULTS: The Arab world produced 189 papers per one million people, about a quarter of the value for other world countries. Four Arab countries (Qatar, Tunisia, Lebanon, and Kuwait) produced more than 695 papers per one million people, exceeding the world average. The average number of citations per paper was 9.2; it rose to more than 15 for papers with international collaboration. At the institutional level, the number of citations showed upward trends, with six institutions having an average citation per paper higher than that of all Arab countries. For the 10 most productive institutions in Arab countries, the percentage of papers involving international collaborations ranged from 42% to 79%; of these, 9% to 29% were led by authors from the same institution. For these 10 most productive institutions, the percentage of papers published in the top quartile journals and with a lead/corresponding author from the institution ranged from 7 to 32%; that percentage drops to 1% to 10% for papers published in top 10% journals. CONCLUSIONS: Although medical research output in Arab countries at both the country and the institution levels has increased over the past 10 years, it is still lagging behind the rest of the world. The percentage of papers involving international collaborations was relatively high, but the majority of these papers were led by authors from outside the local institution, particularly when published in the top 10% journals.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.051 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".