Trauma‐Related Symptom Improvement in Multimodal Triphasic Trauma Therapy: Findings From a Community‐Based Study
Bibliographic record
Abstract
OBJECTIVE: This single-arm effectiveness study explored changes in trauma-related symptoms-including dissociation, depression, anxiety, sexual issues and sleep disturbances-throughout a multimodal, phased trauma intervention, to explore treatment response in real-world settings with varied populations and complex clinical presentations, as well as varied degrees of clinician experience. METHOD: Symptom change was assessed among participants undergoing a triphasic trauma therapy called trauma practice. Data were collected at five time points: pretreatment (n = 41), Phase 1 (n = 37), Phase 2 (n = 25), Phase 3 (n = 20) and follow-up (n = 16). Participants completed self-report measures at the start of therapy, after each therapy phase and 6 months post treatment. The average age of participants was 37.6 years (SD = 12.5). Approximately 63.8% identified as female, 55% were born in Canada and 47.5% identified as Caucasian. RESULTS: The findings revealed statistically and clinically significant reductions in symptoms across all measured domains. On average, participants transitioned from clinically elevated levels of dissociation, anxiety, depression, sexual difficulties and sleep disturbances at baseline to non-clinical levels by the end of therapy. Moderate to large effect sizes, clinically significant reliable change indices and sustained treatment gains were demonstrated at follow-up. CONCLUSION: These results suggest that trauma practice holds promise as an effective intervention for trauma in community clinical settings.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".