Therapeutic enactment : a case study of the experience of a Canadian military veteran
Bibliographic record
Abstract
Much of the literature on therapeutic treatments for military personnel who have suffered an operational stress injury or PTSD from active duty focuses on individual therapy models. While models of group therapy for military veterans do exist, limited understanding of the impact of their process is known, particularly with Canadian military veterans. This study attempts to understand the experience of one particular type of group therapy called Therapeutic Enactment (TE) through the lens of a Canadian Armed Forces veteran. A case study research design was employed for this study. The participant of this study was a male, Canadian military veteran who had suffered an operational stress injury related to his role in the military and had completed at least one prior Therapeutic Enactment in relation to this difficulty. Data was collected through a 1-hour semi-structured, open-ended interview with a participant via a virtual recorded interview over Zoom. The results of the study were analyzed using the six-phase process of thematic analysis from Braun and Clarke (2006). Four main themes, each with two or three subthemes emerged from this data analysis. The four major themes were: 1) Trust, 2) Beneficial aspects of Therapeutic Enactment, 3) Challenges in Therapeutic Enactment, and 4) Recommendations for future Therapeutic Enactments. This study contributes to our overall understanding of how Therapeutic Enactment is experienced and gives guidance for practical application for clinicians. Implications for future research are also discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.048 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| 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".