A bibliometric and knowledge-map analysis of psychological violence from 2003 to 2022
Bibliographic record
Abstract
Background: Psychological violence is a serious global public health and social issue, attracting increasing research. Its adverse effects on individuals and society are significant. Given the negative impacts of psychological violence and the importance of maintaining mental health, we employed a bibliometric analysis of the literature on psychological violence over the past 20 years on a global scale. Objective: We aimed to elucidate the current research hotspots and development trends in psychological violence using bibliometrics and a visualization analysis, and provide ideas for related research. Methods: We searched three databases in the Web of Science Core Collection to obtain data from January 1, 2003, to December 31, 2022, and utilized VOSviewer, CiteSpace, and Scimago Graphica software to visualize authors, journals, countries, institutions, and collaboration and keyword networks. Results: Ultimately, 4,387 publications related to psychological violence were identified; the top three countries for the number of publications were the United States, England, and Canada, in that order; The top three institutions were the University of Toronto, King's College London, and Columbia University. Of 15,681 authors, the average publication rate was 0.28 articles per author, with Rodriguez-Carballeira A from Spain publishing the most articles; The top three disciplinary distributions were psychiatry, family Studies, and clinical psychology; Research hotspots included causes, harms, evaluation strategies, and interventions related to psychological violence. Conclusion: The increasing number of publications suggests greater interest among researchers in the interconnected domains of psychological violence, with the ongoing research reflecting stability and maturity. The current research focus is on the risk factors, manifestations, and consequences of psychological violence. In the future, potential emerging research trends include exploring measurement methods for psychological violence and identifying mitigation strategies. The global community has developed a well-established foundation for cooperation. However domestic research is lacking, indicating the need for enhancement and increased collaboration among researchers, institutions, and countries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.185 | 0.245 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".