Hot spots and frontiers of postpartum depression research in the past 5 years: A bibliometric analysis
Bibliographic record
Abstract
Objective: This study applied the bibliometric method to analyze the hotspots and frontiers in the field of postpartum depression research from 2017 to 2021. Methods: A computer-based search for studies related to postpartum depression (PPD) was conducted in the Web of Science Core Collection from 2017 to 2021. Using CiteSpace 5.8. R3 visualization software, this paper analyzed the hot spots and frontiers of postpartum depression research from countries, institutions, authors, cited references and keywords, and drew corresponding knowledge maps. Results: A total of 3,789 articles were retrieved, and the annual publication volume showed a steady increase. The countries and institutions with the most publications were the United States and the University of Toronto, respectively. Cindylee Dennis was the most productive author. The most frequently cited and centrally cited articles were meta-analyses on risk factors associated with antenatal depression or anxiety and meta-analyses on the prevalence of antenatal or postnatal anxiety, respectively. The research hotspots in the field of postpartum depression in the past 5 years mainly focused on the risk factors for PPD, and the impact of the COVID-19 epidemic on PPD. Providing various aspects of social and family support to women with PPD may be a research trend in this field. Conclusion: This study provides a trend and frontier in the field of postpartum depression, and valuable information for researchers to find potential partners and partner countries, and a reference for future research topics and development directions.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.186 | 0.191 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 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".