Trajectories of posttraumatic stress symptoms during and after Narrative Exposure Therapy (NET) in refugees
Bibliographic record
Abstract
BACKGROUND: Trauma-focused therapy approaches are recommended as treatment for posttraumatic stress disorder (PTSD). This includes the treatment of trauma-related suffering in refugee populations. However, there is a lack of knowledge about symptom trajectories in refugees living in volatile conditions. This has led to fear of "retraumatisation" and general skepticism in clinicians concerning the use of exposure therapy. METHODS: To test the relevance of this concern, we investigated PTSD symptom trajectories and potentially influencing factors during the course of Narrative Exposure Therapy (NET) in a refugee sample living in Germany. Refugees filled out the PTSD Checklist prior to each treatment session and also during follow-up interviews. Therapists continuously documented positive and negative life events as well as the content of the treatment sessions. Additionally, structured clinical interviews were conducted pre-treatment and at follow-up time points. RESULTS: On average, clients presented with substantial decreases in PTSD symptoms already during and after NET. However, symptom trajectories differed and ranged from fast responders to slow responders to no immediate response during treatment. Importantly, a persistent worsening of symptoms was not observed, also not after exposure to the most distressing events. In contrast, stressful life experiences seemed to aggravate PTSD symptoms. CONCLUSIONS: Consistent with earlier studies, NET leads to clinically and behaviorally relevant reductions in PTSD symptoms both throughout and following treatment in refugees living in volatile conditions. Concerns about imaginal exposure in refugees were not substantiated. While stressful life events contributed to transient symptom increases, they weren't found to prevent the overall effectiveness of NET. TRIAL REGISTRATION: NCT02852616.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".