A Bibliometric Analysis of Research Trends in Spousal Support for Breast Cancer Patients
Bibliographic record
Abstract
Background: The annual rise in breast cancer incidence is a significant issue that threatens women’s health and imposes various physical and psychological effects on male spouses, while these spouses often serve as the primary source of social support for patients. Purpose: To conduct a systematic analysis of publications, countries, institutions, journals, disciplines, authors, keywords, and references related to spousal support for breast cancer patients using bibliometric methods. Methods: We searched the Web of Science Core Collection (WOSCC) for publications related to spousal support for breast cancer patients from January 2004 to December 2024. CiteSpace (6.4 R1, 64-bit Advanced Edition) and Microsoft Office Excel 2019 were used for bibliometric analysis and chart generation. Results: Our study analyzed 672 articles in the WOSCC database on spousal support for breast cancer patients over the past two decades. These publications have exhibited a trend of fluctuating growth. Research area primarily focuses on oncology, psychology, and nursing, with findings mainly published in Psycho-Oncology and Supportive Care in Cancer . The United States, Canada, and Australia lead this research domain, with the University of California system, Duke University, and Harvard University being the principal research institutions. Laura S. Porter and Donald H. Baucom are among the most prolific authors. The main keyword clusters include #1 caregiving burden, #2 quality of life, #3 sexual health, #4 qualitative study, #5 dyadic coping, and #6 marital status. The references focus on social psychology, intimate relationships, emotional communication, and coping interventions. Conclusion: This bibliometric study analyzes research on spousal support for breast cancer patients during the last two decades, outlining the publications, countries, institutions, journals, disciplines, and authors that have significantly influenced the field. Emerging trends in research on spousal support for breast cancer patients emphasize valuing the caregiving burden endured by spouses, exploring their support experiences, identifying spousal support barriers, and addressing intimacy challenges. Keywords: breast cancer, spouse support, visual analysis, cite space, review, research hotspots
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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 | 0.019 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.229 | 0.295 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".