Trends in Research on Traditional Chinese Health Exercises for Improving Cognitive Function: A Bibliometric Analysis of the Literature From 2001 to 2020
Bibliographic record
Abstract
Although previous studies have investigated the ability of traditional Chinese health exercises (TCHEs) to improve cognitive function, few have utilized bibliometric analyses to address this topic. We aimed to investigate the current status of and developmental trends in this field from 2001 to 2020. We searched the Web of Science Core Collection (WoSCC) for all research publications on cognitive function in relation to TCHEs. CiteSpace V was used to analyze the number of papers, countries, institutions, journals, authors, and citations. We identified hotspots and trends in the field by drawing co-citation reference and co-occurrence keyword maps. From 2001 to 2020, 406 relevant articles were published in the WoSCC, with a gradual increase in the annual number of publications. The three countries/regions with the most publications were the Chinese mainland, the United States, and Canada. Six universities from China and four from the United States were identified as the top 10 institutions. Most research was conducted at universities. Evidence-based Complementary and Alternative Medicine was identified as the most productive journal. Together, these findings indicate that TCHEs have received increasing attention as a method for improving cognition.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.100 | 0.151 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".