Research hotspots of mutuality: A visualization analysis based on CiteSpace
Bibliographic record
Abstract
Objective To understand the current situation, hotspot and development trend of the research about mutuality in the past 20 years in China and abroad, so as to provide reference for the research about mutuality in China. Methods CiteSpace software and bibliometric online analysis platform were used to visually analyze the literature about mutuality research published between January 1, 2003 and March 1, 2023. Results A total of 11 Chinese literatures and 1374 English literatures were included. The number of annual publications is on the rise, and the countries with large research contributions were the United States, the UK and Canada. Foreign research institutions were mainly concentrated in colleges and universities, with close exchanges and cooperation, while domestic research institutions were few and dispersed, with less cooperation and exchange. The frontiers of mutuality research were “quality of life” and “caregivers” in China. Foreign study of mutuality mainly focused on impact and caregivers, and extended the scope of the study to nursing of cancer and chronic diseases, as wells as interplay of mental health between patients and their caregivers. Conclusion The study of mutuality has a wide scope. It has been used extensively in psychology, medicine and other fields in foreign countries. However, the study of dependency relationship has just been started in our country. Therefore, Chinese scholars should combine our national conditions and culture, draw lessons from mature research abroad, and innovate and deepen on this basis. (目的 了解国内外近20年相依关系研究的现状、热点和发展趋势, 为我国相依关系研究提供参考和借鉴。方法 使用CiteSpace软件和文献计量在线分析平台对国内外2003年1月1日至2023年3月1日相依关系研究文献进行可视化分析。结果 中文文献共纳入11篇, 英文文献共纳入1374篇。年度发文量呈上升趋势, 研究贡献较大的国家是美国、英国和加拿大; 国外研究机构主要集中于高校, 交流合作较紧密; 国内研究机构较少, 且分布相对分散, 合作交流较少。国内相依关系的护理研究前沿为“生活质量”和“照顾者”, 已在相关疾病领域, 国外集中于将相依关系应用于研究前沿为“impact”、“caregivers”, 并已延伸到癌症、慢性病等护理等领域, 对患者和照顾者心理健康问题的相互影响进行了深入了解。结论 相依关系研究范围较为广泛, 国外已将其大量应用于心理学、医学等多个领域, 而我国对于相依关系的研究才起步不久。国内学者应结合我国国情和文化, 借鉴国外成熟研究, 在此基础上创新和深入。)
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.115 | 0.101 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".