Current status and hotspots in breast cancer patient self-management research: a bibliometric and visual analysis via CiteSpace (Preprint)
Bibliographic record
Abstract
BACKGROUND Breast cancer is the most common cancer in women and the leading cause of cancer-related deaths among women worldwide.With significant improvements in breast cancer screening, diagnosis and treatment, the number of breast cancer survivors has significantly increased.Individuals with chronic illnesses often develop self-management skills as they cope with their condition, and these include: management of disease symptoms, detection of physiological and psychosocial changes, and lifestyle changes.Fewer studies have systematically summarised the hotspots, pathways and trends in self-care of breast cancer patients, and information on the history, current status and future trends of studies related to self-management of breast cancer patients is incomplete. OBJECTIVE Purpose: To analyze the current status, hotspots, and research trends related to self-management in breast cancer patients from 2005 to 2023 using Citespace on the Web of Science core database. METHODS Method: A search was conducted in the WoSCC from January 1, 2005, to August 31, 2023. The literature was visualised and analysed by CiteSpace 6.1.R6 for publication time, number of articles, country distribution, institutional distribution, reference co-citation, and keywords. RESULTS Results: A total of 1,413 English-language documents were included in the research on self-management of breast cancer patients from 2005 to 2023. The USA had the highest amount of issuance, while the University of Toronto had the most among institutions. The reference with the highest number of co-citations was "Self-Management: Enabling and empowering patients living with cancer as a chronic illness. "High-frequency keywords are quality of life, chronic disease, self-management, patient education, randomised controlled trials, education, intervention. These keywords formed 11 clusters related to the content of the intervention, the way of the intervention, outcome indicators, keyword burst analysis predicted that future research hotspots would focus on patient needs, psychological distress, internet technology, and mobile apps. CONCLUSIONS Conclusions: The research on breast cancer self-management is expanding rapidly. To further promote the development in this field, it’s crucial to strengthen cooperation and communication between different countries/regions and institutions. The findings suggest that there’s a need for more research in this field, particularly in areas such as patient needs and the use of technology to improve breast cancer patient self-management. Additionally, our findings offer suggestions for future research.
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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 | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.233 | 0.313 |
| Science and technology studies | 0.001 | 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) | 0.012 | 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".