Current status and hotspots in breast cancer patient self-management research: A bibliometric and visual analysis via CiteSpace
Bibliographic record
Abstract
BACKGROUND: Breast cancer remains a leading cause of cancer-related mortality worldwide. Patient self-management plays a pivotal role in enhancing outcomes and quality of life for individuals affected by this disease. This study employed bibliometric and visual analysis techniques utilizing CiteSpace to elucidate the current status and research hotspots in breast cancer patient self-management from January 1, 2005, to August 31, 2023. METHODS: A comprehensive search was conducted in the Web of Science Core Collection (WoSCC). The retrieved literature was subjected to visualization and analysis using CiteSpace, focusing on publication timeline, article count, geographical distribution, institutional affiliations, journal sources, reference co-citation networks, and keyword analysis. RESULTS: The analysis encompassed 1413 English-language documents. The United States emerged as the most prolific contributor, while the University of Toronto demonstrated the highest institutional output. The two-map overlay revealed prominent citation paths, indicating strong interconnections between publications in "Medicine, Medicine, Clinical" and "Health, Nursing, Medicine," as well as "Psychology, Education, Health" and "Health, Nursing, Medicine." The most frequently co-cited reference was "Self-Management: Enabling and Empowering Patients Living with Cancer as a Chronic Illness." High-frequency keywords identified included quality of life, chronic disease, self-management, patient education, randomized controlled trials, education, and intervention. These keywords formed 11 distinct clusters related to intervention content, methodologies, outcome indicators, and emerging research trends. Keyword burst analysis predicted future research hotspots focusing on patient needs, psychological distress, Internet technology, and mobile applications. CONCLUSIONS: Research in breast cancer self-management is experiencing significant growth. Enhanced collaboration between countries, regions, and institutions is imperative. Further investigation is warranted, particularly in the domains of "quality of life," "patient education," and "mobile health." These findings provide valuable insights to guide future research directions in this critical 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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.214 | 0.231 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".