The Mapping Knowledge Analysis of Exercise Intervention for Drug Dependence Research at Home and Abroad
Bibliographic record
Abstract
Based on the 542 articles with the theme of “exercise intervention and drug dependence” from 1991 to 2018 included in WOS core collection database, this study analyzed the annual output quantity, country/region,high-yield authors, subject distribution, high-frequency keywords, keywords time zone view, high-frequency classical literature, etc. by using CiteSpace, as a mean of visualization. The purposes of this study were to analyze structural characteristics, quantitative relation, research hotspots and evolution in the field of exercise intervention and drug dependence. RESULTS: The number of publications on exercise intervention and drug dependence was on the rise. The United States, the United Kingdom and Canada were in the world leading position in the exercise intervention and drug dependence, and Shanghai University of Sport occupied the dominating position in China. Universities and hospitals were the important positions. The research involved several interdisciplinary subjects, such as neuroscience, drug abuse, public environment and occupational health, pharmacology and sports science, etc. The research hotspots focused on tobacco and alcohol-dependent population, mainly taking exercise intervention or physical activity as the independent variable, withdrawal symptoms, behavioral cognition, craving degree, health risk and other indicators as the dependent variable, with little research on drug dependence population, especially the optimal form and neural mechanism of exercise intervention were still unclear.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.094 | 0.097 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".