Bibliometric Analysis on Antenatal Depression: A Comparison of Research between Web of Science and China National Knowledge Infrastructure
Bibliographic record
Abstract
Background: Antenatal depression (AD) has adverse effects on mothers and children. While pregnant women in China are given less attention in terms of their mental health, in contrast, AD has resulted in increased interest at the international level. Methods: This study reviewed 1881 studies on AD from the Web of Science (WOS) and China National Knowledge Infrastructure (CNKI), using the bibliometric method in CiteSpace, to systematically analyze the research status, research hotspot, and potential trends of research on AD in China and abroad. Results: The results showed that: (1) There are 511 papers from the United States, followed by 210 from England, 136 from Australia, and 116 from Canada. Furthermore, articles from these four countries have the highest influence. And that the quantity and influence of papers published in China are relatively low; (2) Institution with the most publications is located in England, and those with the most influence are located in Australia and the United States; there are few Chinese institutions that publish on AD; (3) Literature on WOS clustered 7 hot topics, while documents on CNKI clustered 6, with similarities and differences; (4) With the passage of time, the researches of AD on CNKI gradually focused on the investigation and intervention of specific groups, while researches on WOS tend to consistently explore the biological and psychological mechanism and variety of intervention measures. Conclusions: It is the goal of China’s research to further explore the mechanisms and influencing factors of AD in order to better implement diversified interventions and improve the quality of life for mothers with AD and their offspring.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.164 | 0.228 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".