Current Research on Matching Trauma-Focused Therapies to Veterans: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Trauma-focused (psycho)therapies (TFTs) are often used to treat post-traumatic stress disorder (PTSD) of (military) veterans, including prolonged exposure (PE), cognitive processing therapy (CPT), and eye movement desensitization and reprocessing. However, research thus far has not conclusively determined predictors of TFTs' success in this population. This scoping review's objectives are 1) to explore whether it is possible, based on currently available evidence, to match TFTs to veterans to maximize their outcomes, (2) to identify possible contraindications and adaptations of TFTs for this population, and (3) to identify gaps in the literature to guide future research. MATERIALS AND METHODS: Standard scoping review methodology was used. "White" and "gray" literature searches resulted in 4963 unique items identified. Following title and abstract screening and full-text analysis, 187 sources were included in the review. After data extraction, a narrative summary was used to identify common themes, discrepancies between sources, and knowledge gaps. RESULTS: Included publications most often studied CPT and PE rather than eye movement desensitization and reprocessing. These TFTs were at least partly effective with mostly moderate effect sizes. Attrition rates were slightly higher for PE versus CPT. There was variance in the methodological quality of the included studies. CONCLUSION: The current literature on TFTs to treat PTSD in veterans contains several knowledge gaps, including regarding treatment matching. Future research should examine effectiveness of these treatments using multiple sources of outcomes, longer time periods, combination with other treatment, outcomes outside of PTSD symptoms (such as functioning), and resilience.
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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 | 0.035 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.028 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".