Mapping publication outputs, collaboration networks, research hotspots, and most cited articles in systematic reviews and meta-analyses of medicine and health sciences in Ethiopia: analyses of 20 years of scientific data
Bibliographic record
Abstract
Abstract Introduction Although the publication of systematic reviews (SR) and meta-analyses (MA) has substantially grown in Ethiopia, no robust study systematically characterized these SR and MA was conducted. Thus, we aimed to map publication outputs, collaboration networks, research hotspots, and most cited SR and MA of medicine and health sciences in Ethiopia. Methods We conducted a bibliometric study of SR and MA published up to December 31, 2021, and systematically searched via PubMed, PsycInfo, EMBASE, and Web of Science databases. We included all SR and MA in medicine and health sciences fields in Ethiopia irrespective of the authors’ affiliation and place of publication. Full records and cited references’ meta-data were extracted from the Web of Science Core Collection database. VOSviewer software was used to perform bibliometric analyses. The relevance of an item (e.g. author, country, or keywords) was measured by its weight based on frequencies using the full or binary counting method) and strength of the link between items was measured using total link strength. Results In total, 422 SR and MA were published between 2001 and 2021 by 14 research groups (i.e. overall, 1,066 authors participated) who affiliated with institutions from 33 countries. The largest number of SR and MA were published by authors affiliated with Debre Markos University, University of Gondar and Bahir Dar University. In addition, strong collaboration was observed among authors affiliated with institutions in Ethiopia, the Netherlands, Australia, and Canada. The identified research hotspots were maternal and child health, depression and substance use, cardiometabolic diseases, infectious diseases, HIV/AIDS, hepatitis and nutrition. The most cited SR was about domestic violence against women published in 2015. The SR and MA were published in 160 journals, with a majority published in PLOS (11%) and BMC (25%) journals. Conclusions In this study, we provide a comprehensive summary of collaboration networks, research hotspots, and most cited SR and MA to gain a deeper understanding of the landscape of SR and MA research in Ethiopia. We believe that our study informs researchers, higher institutions, and policymakers about research hotspots and gaps in medicine and health sciences research in Ethiopia. The national and international collaboration is promising, and a concerted effort among researchers, policymakers and funding agencies could increase research outputs and broaden research areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.056 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.114 | 0.120 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".