Treating Posttraumatic Stress Disorder in Military Populations
Bibliographic record
Abstract
Military and Veteran populations experience higher rates of posttraumatic stress disorder (PTSD) compared to civilians. While trauma focused psychotherapies are generally recommended as first-line treatments, the effectiveness of various treatments in military populations requires further investigation. This meta-analysis aims to synthesize the current literature regarding effectiveness of psychotherapies, pharmacotherapies, and combination treatments for PTSD in military populations. This preregistered review (PROSPERO: CRD42021245754) was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta Analyses and Cochrane guidelines. A search was conducted using PsycINFO, MEDLINE, Embase, CINAHL, and ProQuest Dissertations and Theses. The final sample included data from 414 studies. Full study methodologies can be found in the published protocol (Liu et al, 2021). =2.17), outperforming both psychotherapies and pharmacotherapies alone. No significant differences were found across control conditions. Findings suggest that integrating psychotherapies and pharmacotherapies may address multiple dimensions of PTSD more effectively than monotherapies. However, these results contrast with the prioritization of trauma-informed psychotherapies over pharmacotherapies, as recommended by the 2023 US Department of Veterans Affairs/Department of Defense guidelines. Future research should focus on subclass analyses and long-term outcomes to refine treatment strategies for PTSD in military populations. Tailoring treatment plans to individual needs remains crucial for optimizing recovery and long-term symptom management.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".