Bibliometric analysis of published articles on perinatal depression from 1920 to 2020
Bibliographic record
Abstract
AIM: To analyze the characteristics and trends in published research on perinatal depression between 1920 and 2020. METHODS: A search strategy in Web of Science identified all published literature on perinatal depression between January 1, 1920, and December 31, 2020. Output from Web of Science was used to analyze bibliometric information, and VOSViewer was used to visualize the networks of linkages between identified publications. RESULTS: There were 16,961 publications identified. Among these publications, there were 82,726 unique authors and 140 countries represented. The United States had the highest frequency of publications (44.6%). Most publications (69.8%) occurred between 2011 and 2020, with the first publication identified in 1928. There were 2197 unique journals identified, with over half publishing only one (n = 948, 43.2%) or two relevant publications (n = 314, 14.3%). Authors with the largest number of publications were Wisner (n = 115), Dennis (n = 95), and Murray (n = 92), while authors with the largest number of citations were Cox (n = 7225), Murray (n = 2755), and O'Hara (n = 2069). LIMITATIONS: While the Web of Science is a representative database identifying the greatest number of relevant articles, it may be unrepresentative of all published literature. CONCLUSION: This is the first study mapping publications on perinatal depression between 1920 and 2020. The rate of publication on perinatal depression has been steadily increasing in recent years with a wide variety of authors, countries, and journals represented. As the field continues to grow, trends may shift as early career researchers emerge and the importance of mental health in low-income countries is prioritized.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.153 | 0.170 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".