Half a Century of Research on Posttraumatic Stress Disorder: AScientometric Analysis
Bibliographic record
Abstract
We conducted a scientometric analysis to outline clinical research on posttraumatic stress disorder (PTSD). Our primary objective was to perform a broad-ranging scientometric analysis to evaluate key themes and trends over the past decades. Our secondary objective was to measure research network performance. We conducted a systematic search in the Web of Science Core Collection up to 15 August 2022 for publications on PTSD. We identified 42,170 publications published between 1945 and 2022. We used CiteSpace to retrieve the co-cited reference network (1978-2022) that presented significant modularity and mean silhouette scores, indicating highly credible clusters (Q = 0.915, S = 0.795). Four major trends of research were identified: 'war veterans and refugees', 'treatment of PTSD/neuroimaging', 'evidence syntheses', and 'somatic symptoms of PTSD'. The largest cluster of research concerned evidence synthesis for genetic predisposition and environmental exposures leading to PTSD occurrence. Research on war-related trauma has shifted from battlefield-related in-person exposure trauma to drone operator trauma and is being out published by civilian-related trauma research, such as the 'COVID-19' pandemic impact, 'postpartum', and 'grief disorder'. The focus on the most recent trends in the research revealed a burst in the 'treatment of PTSD' with the development of Mhealth, virtual reality, and psychedelic drugs. The collaboration networks reveal a central place for the USA research network, and although relatively isolated, a recent surge of publications from China was found. Compared to other psychiatric disorders, we found a lack of high-quality randomized controlled trials for pharmacological and nonpharmacological treatments. These results can inform funding agencies and future 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 | 0.040 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.100 | 0.138 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".