Exploration of Trauma-Oriented Retreats: Quantitative Changes in Mental Health Measures for Canadian Military Members, Veterans and Royal Canadian Mounted Police with Posttraumatic Stress Disorder and Moral Injury
Bibliographic record
Abstract
Background: Military members, veterans, and public safety personnel have been noted to have a higher risk of exposure to potentially traumatic events and potentially morally injurious events resulting in operational stress injuries (OSI) such as posttraumatic stress disorder (PTSD) and moral injury (MI). Treatments that can quickly and effectively address these conditions are desperately needed. The purpose of this research was to identify the impact of participation in a non-evidence-based trauma-oriented retreat for the above populations experiencing PTSD and MI. Methods: This study was an embedded mixed-methods longitudinal study with parallel repeated quantitative measures designed to evaluate outcomes at 1, 3, 6, and 12 months after completion of the retreat. Results: Analysis showed a statistically significant reduction in self-reported symptoms of PTSD, anxiety, stress, depression, MI, anger, and emotional dysregulation pre/post-retreat, and an increase in resilience. Self-reported longitudinal results did not see a change in symptom scores, with participants continuing to maintain their clinical diagnoses post-retreat. Conclusions: The results from this study illustrate that trauma-oriented retreats may be a complementary treatment modality for OSI-related conditions but should not be seen as a first-line treatment option. Program evaluation, determination of the evidence-based nature of retreats, and standardization are yet needed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".