CiteSpace-based visualization analysis of domestic and foreign researches on post-traumatic stress disorder in recent five years
Bibliographic record
Abstract
ObjectiveTo understand the research status of post-traumatic stress disorder （PTSD） at home and abroad in recent five years， and to grasp the research frontiers and hot spots in this field.MethodsTwo electronic databases， Web of Science and China National Knowledge Infrastructure （CNKI）， were retrieved for the literature published from January 1， 2017 to December 31， 2021. A total of 8 505 literatures were included， then the visualization analysis of the number of publications， authors， countries， institutions and keywords was conducted based on Microsoft Excel and CiteSpace software.Results① The number of publications in domestic and foreign showed an increasing trend in recent five years. ② In foreign literature， the top five countries in terms of the number of publications were the United States， the United Kingdom， China， Australia and Canada， with Canada having the highest centrality （0.18）. ③ Both domestic and foreign research institutions were dominated by universities. ④ In terms of the number of articles published， the top three foreign scholars were Bryant RA， Ressler KJ and Greenberg N， and the top three Chinese scholars were Wu Xinchun， Li Yuefeng， Yan Xingke and Zhang Guiqing （tied for the third place）. Compared with foreign authors， the number of articles published by Chinese scholars was relatively small. ⑤ In terms of research keywords， PTSD and depression were of more concern in both domestic and foreign.ConclusionIn recent five years， PTSD has been a hot topic at home and abroad， with both domestic and international studies focusing on PTSD and depression， and strengthening international exchanges may help promote progress in the field of PTSD 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.079 | 0.073 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".