Effect of the COVID-19 pandemic on maternal health research: emerging trends and bibliometric analysis
Bibliographic record
Abstract
Maternal health research (MHR) includes studies of pregnancy care, maternal and infant mortality, and family planning. This study examined maternal health research before, after, and during the COVID-19 pandemic. A bibliometric analysis of 11,422 papers in the field of MHR literature from the Scopus database that were published between 2016 and 2023. The MHR has increased annually by 10.45%. The number of publications has grown significantly during the previous eight years. In 2016, there were 1036 articles published, followed by 1868 in 2022 and 1086 in 2023. Following COVID-19, there was an increase in publications between 2020 and 2023, and productivity peaked in 2022 with 1868 articles of all publications throughout the data period. The United States, the United Kingdom, Australia, Canada, and Ethiopia are the top productive countries in MHR. The COVID-19 pandemic has significantly contributed to the thematic expansion of the MHR and the network analysis revealed distinct variations in the number of researchers. In the aftermath of the COVID-19 pandemic, Ethiopia has experienced significant growth in the field of MHR. Research on MHR currently focuses on antenatal care, maternal health, pregnancy, public health, and preterm birth.
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 | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.136 | 0.222 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".