Research hotspots and trends analysis of traditional Chinese medicine nursing for stroke based on co-word analysis
Bibliographic record
Abstract
Objective To explore the research hotspots and trends of traditional Chinese medicine (TCM) nursing for stroke. Methods Literatures on TCM nursing for stroke were retrieved in Chinese National Knowledge Infrastructure (CNKI) , WanFang Data, VIP, China Biological Medicine (CBM) from building database to September 2018. The data management tools included BICOMB 2.0, SPSS 22.0 and UCINET 6.0. The research hotspots and trends of TCM nursing for stroke were analyzed with the methods of co-word analysis, clustering analysis and social network analysis. Results A total of 7 253 literatures were included involving 17 236 keywords and 30 high-frequency keywords. Clustering analysis showed that research hotspots of TCM nursing for stroke focused on the application of auricular buried beans in symptomatic nursing for stroke, negative emotion management of stroke, body function treatment after stroke and its research on model and TCM rehabilitation appropriate technology of stroke sequela. Social network analysis showed that scope and quantity of research hotspots of TCM nursing for stroke expanded year by year, exploration field was excavated and deepened constantly and inheritance emerged gradually. Conclusions Co-word, clustering and social network analysis objectively reflects the research hotspots and trends of TCM nursing for stroke which could provide the information support for researchers and clinical workers and provide a reference for research selected topic as well as clinical practice. Key words: Stroke; Traditional Chinese medicine nursing; Research hotspots; Co-word analysis; Social network analysis
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.035 | 0.039 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".