Research Hotspots and Trends in Qualitative Perinatal Nursing: A Bibliometric Analysis
Bibliographic record
Abstract
This study aimed to understand the research hotspots, current status, and development trends of qualitative research in perinatal nursing from the establishment of the database to December 31, 2023. Using the Web of Science core database as the data source, English language articles of perinatal nursing qualitative research published during this period were retrieved. And we analyzed the annual number of papers, national and institutional co-authorship network, journal distribution and citation, co-authorship and co citation of authors, keyword co-occurrence network and clustering. A total of 751 articles were finally included. The number of papers on qualitative research of perinatal nursing s has shown a continuous upward trend. The number of articles published in the United States ranks first, and exhibited the highest total link strength. The University of California, San Francisco was the institution with the largest number of publications. Research hotspots in this field include the evolving concept of maternal "care," the intersection of mental health and psychological support, childbirth decision-making, and maternal experiences. Emerging research frontiers involve personalized health management for pregnant women and prenatal healthcare services. In recent years, qualitative research in perinatal nursing has continued to develop. Future studies should perform ongoing bibliometric analyses across multiple databases to capture real-time research hotspots and accurately track trends in this field.
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.195 | 0.285 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".