Bibliometric analysis of parental anxiety and postpartum depression across the perinatal period from 1920-2020: A protocol
Bibliographic record
Abstract
ABSTRACT Introduction Throughout the perinatal period from pregnancy to the first year postpartum, both men and women experience significant physical, psychological, and social changes which may increase their risk of a mental illness, including anxiety and depression. There has been significant growth in the frequency literature around anxiety and depression across the perinatal period over the past decades with significant variation in definition, measurement outcomes, and populations. To focus future research and identify gaps, it is important to explore current patterns and trends in the current literature. Objective The objective of this bibliometric analysis is to analyze the characteristics and trends in published research on anxiety and depression across the perinatal period from January 1, 1920 to end of 2020. Inclusion criteria All published literature in Web of Science on perinatal anxiety and depression from January 1, 1920 to December 31, 2020. Methods Web of Science will be used to analyze bibliometric information through their built-in analysis feature and citation report that generates a list of leading publications, publication years, document types, authors, source titles, countries/regions, organizations, and research areas. VOSViewer will be utilized to analyze and visualize the networks of linkages between the identified reports, including bibliometric networks, including co-authorship, co-occurrence, and co-citation, as well as co-occurrence between keywords. Conclusion The findings from this study will provide useful information to guide future work on perinatal anxiety and depression. This bibliometric review will provide an overview of the work to date in perinatal mental health, identify key contributions to the field, and identify knowledge gaps and future 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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.042 | 0.049 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".