Healing trauma through theatre: A therapeutic vignette from the DE-CRUIT Veterans’ theatre program
Bibliographic record
Abstract
As the use of arts-based approaches to support Veterans' mental health continues to expand, there is growing interest in the mechanisms underlying their delivery. This article describes the DE-CRUIT group treatment program that uses theatre to support Veterans in their transition to civilian life and in coping with mental health struggles. Components of the DE-CRUIT program are outlined, in particular, the use of Shakespeare in addressing Veterans' trauma. In the program, Veterans immerse themselves in Shakespeare's verse with an emphasis on the many Veteran and military characters throughout Shakespeare's plays. They examine the Shakespearean monologue form and subsequently compose their own personal trauma monologues. Here, the experience of the program is illustrated through the case vignette of an African American woman Veteran who experienced military sexual trauma. After completing the program, she was able to disclose her abuse to family members for the first time. She also continued to write about her experiences and has now performed her spoken-word work publicly on stage. The article includes recommendations for expanding the DE-CRUIT program, as well as for including family members in the DE-CRUIT treatment process.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".