Multi-Modal Motion-Assisted Memory Desensitization and Reconsolidation (3MDR) Treatment for Postpartum Posttraumatic Stress Disorder (PTSD) from Grief: A Case Report
Bibliographic record
Abstract
The well-being of postpartum mothers can be significantly affected by posttraumatic stress disorder (PTSD) and grief. Success with standard PTSD interventions for this population has been inconsistent. Multi-modal motion-assisted memory desensitization and reconsolidation therapy (3MDR) is an intervention that has been studied favorably with military and veteran populations with PTSD and related conditions. Minimal research is available, however, regarding its application in the civilian population. This case report investigates the use of 3MDR in facilitating trauma and grief processing and reducing PTSD symptoms in a 35-year-old postpartum mother with treatment-resistant PTSD and grief undergoing 3MDR. Qualitative data collected throughout the 3MDR intervention and at the 3-, 6-, and 10-month follow-ups were thematically analyzed. The participant reported feeling increased control over her life after 3MDR and reduced adverse reactions to normally distressing events. During the follow-up sessions, the overarching trauma process was one of peeling back layers of her experiences. The following 3 themes emerged: gaining control in chaos, shifting from an ideal to a real perspective, overcoming trauma and moving forward in life. This case report suggests that 3MDR's immersive, motion-assisted, patient-centered psychotherapeutic approach and strong therapeutic relationship facilitated unique trauma and grief processing, empowering meaningful progress where prior interventions had failed.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".