Positive side effects in trauma-focusing PTSD treatment: Reduction of attendant symptoms and enhancement of affective and structural regulation.
Bibliographic record
Abstract
OBJECTIVE: Trauma-focusing treatments such as eye movement desensitization and reprocessing (EMDR) are highly effective in reducing the core symptoms of posttraumatic stress disorder (PTSD), for example, intrusive memories and flashbacks, hyperarousal, and avoidance. Additionally, suffering from PTSD is often accompanied by a broader set of mental comorbidities and complaints such as depression, anxiety disorders or somatization, and disturbed self-regulation abilities. According to the Adaptive Information Processing model (Shapiro, 2001), the processing of pathogenic memories can help not only to reduce the PTSD symptoms but also accompanying complaints additionally. METHOD: In an eye movement desensitization and reprocessing treatment study of 116 patients suffering from PTSD, we targeted the course of additional symptoms and structural skills using the Symptom Checklist-90 SCL-90, Beck Depression Inventory, Toronto Alexithymia Scale-20, and Hannover Selbstregulationsinventar in a pre-post design. RESULTS: The results showed that apart from alleviating the PTSD symptoms, exposure-based treatment of pathogenic memories led to a significant decrease in accompanying symptoms such as depression, anxiety, and somatization. Furthermore, patients improved their structural abilities with regard to emotional perception and differentiation, controlling impulses, tolerating frustration, and regulating self-esteem. CONCLUSION: PTSD core symptoms and comorbid complaints are closely interlinked and can be seen as a traumatic-stress cluster, which is accompanied by significant impairments in self- and emotion regulation. Therefore, treatment concepts should explicitly foster emotional processing and structural abilities to target the posttraumatic stress responses entirely. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".