Effectiveness of Residential and Intensive Outpatient Programs for the Treatment of Post-Traumatic Stress Disorder in Active Military Personnel and Veterans: A Meta-Analytical Review
Bibliographic record
Abstract
The care and services offered in the treatment of active military personnel and veterans with PTSD take a variety of forms, ranging from residential to outpatient treatment programs. The differences in the organization of care between these programs make comparisons difficult; however, an intermediate alternative exists in the form of intensive outpatient programs (IOPs), whose organization and range of care are closer to that offered in residential programs. This review compares and evaluates the effectiveness of residential programs to that of IOPs in the treatment of PTSD in active military personnel and veterans. Nine databases were searched from September/November 2022 to include primary studies evaluating the treatment of PTSD in active military personnel and veterans in residential programs and IOPs. Results were summarized in a narrative synthesis. A meta-analysis using a random effects model examined changes in standardized mean differences in PTSD symptom scores at baseline and discharge. Thirty-two studies in 41 publications were included. There was a notable decrease in PTSD symptom scores at the end of treatment in both programs, and no significant difference was found between them. However, IOPs effectiveness may be influenced by patient type (active military personnel or veterans). Regardless, our results suggest a positive effect of both types of programs on reducing PTSD symptoms. It is essential to be aware of the constraints inherent in the literature on the subject, including the lack of comparative studies, the potential impact of comorbidities, and the differential response of active military personnel and veterans to similar treatments.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".