Research on Neonatal Conditions in Africa: Volume, Impact, Thematic Spectrum, and Collaboration from a Bibliometric Perspective
Bibliographic record
Abstract
The literature has addressed the negative impact of poor neonatal conditions (NC) across regions. This has drawn attention to the need to improve NC, particularly in Africa. NC research can make an important contribution. However, there is no study dedicated to this topic in Africa. Through a bibliometric analysis, we arrive at outputs that can inform scientists in planning ongoing or new NC research and those involved in developing and implementing strategies to combat poor NC. Using bibliometrics, the study identified the scientific knowledge on NC between 2000 and 2019, its visibility in the community, the main topics researched, and collaboration patterns.The results show that knowledge on NC has increased between 2000 and 2019, it is concentrated in a few African countries (Egypt, South Africa, Nigeria, Tanzania and Kenya), its visibility is below the world average, in general, maternal mortality is the most researched topic and collaborative activities are frequently, mainly international research collaboration (IRC), being the United States of America (USA) and the United Kingdom (UK) the main partners (they participated in 57% and 28% of all articles with IRC). The collaboration networks are fragile as 43%-67% of all links represent one article in 20 years.Ongoing or new research on NC in Africa should consider the main African players and their partners. There is a need to implement strategies to increase NC knowledge in other African countries, expand and strengthen the collaboration networks and diversify the sources of knowledge.
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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.131 | 0.232 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".