Therapeutic use of art with active duty military members and Veterans with PTSD: A scoping review
Bibliographic record
Abstract
Introduction: Creative arts therapies and arts-based interventions are used in treatment programs and have demonstrated a positive impact on the course of certain mental disorders. Military personnel can face traumatic experiences during their service that can potentially lead to the development of psychological trauma. Although evidence-based approaches provide a solid foundation for treatment, challenges include low retention rates, and they may not adequately address the unique psychological and emotional experiences of military personnel. Therefore, it is important to investigate various treatments for posttraumatic stress disorder (PTSD), including complementary ones, such as creative arts therapies and arts-based interventions. Methods: This scoping review examined peer-reviewed journal publications in several languages concerning various creative arts therapies and arts-based interventions used with active duty military members and Veterans. The recommended Joanna Briggs Institute approach for source selection and data extraction was followed. Results: A total of 4,090 publications were examined, with 28 selected for extraction: 14 used quantitative methodology to investigate the impact of art on PTSD symptoms, 12 used qualitative methodology, and two included both. The findings indicate differing arts-based approaches and key methodologies used for PTSD assessment, as well as the fundamental outcomes on PTSD symptoms resulting from these approaches. Discussion: Included publications used six categories of art activities: visual, performing, literary, crafts, therapeutic techniques, and cultural and educational activities. Activities were led by psychologists, art therapists, artists, songwriters, musicians, and dancers. Based on the gaps uncovered in the included studies, potential directions for future research are recommended.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".