Perinatal Depression Research Trends in Canada: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Perinatal depression refers to a major depressive episode that begins during pregnancy or within four weeks after childbirth and persists through the first year postpartum. Perinatal depression is one of the most common complications of pregnancy, with significant adverse maternal and infant outcomes. Numerous reviews and policy guidelines have emerged from Canada; however, a bibliometric analysis that focuses not only on the international sources for perinatal depression research, but also on Canadian sources, has not been undertaken. Purpose: To provide insight on perinatal depression publications conducted by researchers affiliated with Canadian institutions, within an international context. Methods: A bibliometric analysis was performed using performance analysis and science mapping techniques, with data retrieved from Scopus until 31 December 2022. The analysis focused on original peer-reviewed publications, applying no language restrictions and ensuring at least one author was affiliated with a Canadian institution. VOSviewer version 1.6.20 was used to generate visual networks for analysis. Results: In total, there were 763 publications identified in 160 different journals. Among these publications, there were 123 institutions represented. At least one author was associated with a Canadian institution per publication. The University of Toronto had the highest frequency of affiliations (n = 313). Most publications (79.55%) occurred between 2011 and 2022, with 2021 as the year with the most publications (n = 80). The journal with the most publications was Archives of Women’s Mental Health (n = 57, 35.65%). Canadian institution-affiliated authors with the largest number of publications were Dennis (n = 57), Oberlander (n = 39), Meaney (n = 38), and Letourneau (n = 37). Conclusion: This is the first study mapping publications on perinatal depression research within a Canadian context. This bibliometric analysis provides a valuable reference for future research by identifying key authors, institutions, journals, and research areas that prioritize perinatal mental health.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.188 | 0.405 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 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".