Research trends and hotspots in the mental health of widowed older adults: a bibliometric analysis
Bibliographic record
Abstract
Background: The mental health of widowed older adults has garnered increasing research attention due to its profound impact on well-being and quality of life. Despite growing scholarly interest, a comprehensive bibliometric analysis of evolving research trends, key topics, and knowledge structures remains scarce. This study aims to identify key research themes, emerging trends, and interdisciplinary linkages to inform future studies on the mental health of widowed older adults. Methods: A bibliometric analysis was conducted using data from the Web of Science Core Collection (2004-2024). CiteSpace, VOSviewer, and the R package "Bibliometrix" were utilized to visualize publication trends, country and author collaborations, keyword co-occurrences, theme analysis, and emerging research topics. Results: A total of 891 articles were analyzed. The United States produced the highest number of publications, followed by China and the United Kingdom, with the United States, England, and Canada exhibiting strong research collaborations. Depression, prevalence and mental health were identified as core research themes, while life satisfaction and social support emerged as growing areas of interest. Citation burst and thematic evolution analyses revealed shifting scholarly interest from clinical and diagnostic concerns towards psychosocial adaptation and person-centered approaches over time. Conclusion: This bibliometric study systematically maps the research landscape, hotspots, and emerging trends in the mental health of widowed older adults over the past two decades. The findings provide valuable insights for researchers seeking to identify key research directions, foster interdisciplinary collaborations, and develop targeted interventions to support the mental well-being of widowed older adults.
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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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.195 | 0.242 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".