Moving Forward from Moral Injury: A Mixed Methods Study Investigating the Use of 3MDR for Treatment-Resistant PTSD
Bibliographic record
Abstract
BACKGROUND: Exposure to trauma and potentially morally injurious events may lead to moral injury (MI). The link between MI and posttraumatic stress disorder (PTSD) may have particularly relevant implications for treatment-resistant PTSD (TR-PTSD). Multi-modal Motion-Assisted Memory Desensitization and Reconsolidation (3MDR), a technology-assisted exposure-based trauma therapy that has been used in the treatment of PTSD, may also be an acceptable modality for patients in the treatment of TR-PTSD and MI. This proof-of-concept study aimed to investigate (1) whether MI co-occurs in military members (MMs) and veterans with TR-PTSD, and (2) the perspectives of MMs and veterans with TR-PTSD utilizing 3MDR for MI. METHODS: This study employed a mixed-methods clinical trial. Military Members and veterans participated in this study (N = 11) through self-reported questionnaires, video recordings of treatment sessions, and semi-structured interviews post-session and post-intervention, with longitudinal follow-up to 6 months. RESULTS: MI scores correlated with self-reported measures of mental health symptoms related to PTSD. The thematic analysis revealed three emergent themes: (1) Realities of War, (2) Wrestling Scruples, and (3) Moral Sensemaking. CONCLUSION: MI was highly correlated with TR-PTSD and themes regarding MI. This result, while preliminary, allows for the postulation that MI may be contributing to the continuation of PTSD symptoms in TR-PTSD, and that 3MDR may be an acceptable modality for addressing these symptoms in MMs and veterans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".