Bibliometric analysis of published articles on perinatal anxiety from 1920-2020
Bibliographic record
Abstract
ABSTRACT Introduction Trends and gaps in perinatal anxiety research remain unknown. The objective of this bibliometric review was to analyze the characteristics and trends in published research on perinatal anxiety to inform future research. Methods All published literature in Web of Science on perinatal anxiety from January 1, 1920 to December 31, 2020 were screened by two reviewers. VOSViewer was utilized to visualize linkages between publications. Bibliometric data were extracted from abstracts. Results The search strategy identified 4,561 publications. After screening, 2,203 publications related to perinatal anxiety were used for the visualization analysis. For the bibliometric data, 1,534 publications had perinatal anxiety as a primary focus. There were 7,910 different authors, over half named only once (55.5%), from 63 countries. 495 journals were identified, with over half (56.0%) publishing only one article. Most articles were published between 2011-2020 (75.9%). In terms of perinatal timing, over half (54.2%) published on antenatal anxiety. Only 6.0% of studies reported on perinatal anxiety in fathers and 56.5% reported on postpartum depression. Limitations Web of Science was solely used, and manual screening of each publication was required. Conclusion This bibliometric analysis found: (1) perinatal is a growing field of research, with publications increasing over time; (2) there is variation in authors and journals; (3) over half of the publications focus on antenatal anxiety; (4) paternal anxiety is understudied; and (5) only 6% of publications came from low and lower-middle income countries. Gaps related to maternal postnatal, and all paternal perinatal anxiety exist.
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 | 0.012 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.274 | 0.288 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".