Sub-Saharan Africa's biomedical journal coverage in scholarly databases: a comparison of Web of Science, Scopus, EMBASE, MEDLINE, African Index Medicus, and African Journals Online
Bibliographic record
Abstract
Objective: This study aims to find out the coverage of biomedical journals published in Sub-Saharan Africa in four authoritative international databases-Web of Science, Scopus, MEDLINE and EMBASE and two Africa-focused scholarly databases-Africa Journals Online (AJOL) and African Index Medicus (AIM). Methods: Lists of active journals that are published in the 46 Sub-Saharan African countries were retrieved from the Ulrich periodical directory to create master journal lists. Unique journals from other databases that were not found in Ulrich were added to the master journal list. The six databases included in this study were searched for journals on the master lists. Results: Only 23 of the 46 Sub-Saharan African countries had at least one biomedical journal. Only about one-quarter (152) of the 560 biomedical journals from Sub-Saharan Africa were found in at least one of the biomedical databases. South African journals accounted for more than 50% of all the Sub-Saharan journals in the international scholarly databases. AJOL contains the highest number of biomedical journals from Sub-Saharan Africa, followed by Scopus and EMBASE. AJOL asserts its importance by covering the highest number of unique journals and having a representative number of journals in all biomedical sub-disciplines. Conclusion: The majority of studies from Sub-Saharan Africa are left out when biomedical evidence-based researchers only retrieve studies from authoritative international databases. Searching Google Scholar and the African research databases of AJOL and AIM would increase the number of studies from the region.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.061 | 0.086 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| 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".