Case Report: Trauma group therapy with karate-do for war-traumatized children and adolescents
Bibliographic record
Abstract
Background: From the viewpoint of health and education, traumatized children and adolescents who have fled from war and conflict zones to Switzerland represent a high-risk group, as they suffer from psychiatric symptoms to an above-average extent and on several levels: somatic, psychological, psychosomatic, and psychosocial. Objectives: The complexity and severity of these problems overwhelm the existing school structures in many cases: There is a clear need for psychotherapeutic interventions here that goes beyond purely verbal conversational therapy and provides an holistic concept. Methods: We propose the following novel approach: "Trauma group therapy with karate-do for war-traumatized children and adolescents" which integrates and applies the evidence-based methods of integrative Budo-Therapy, trauma-focused Cognitive Behavioral Therapy (TF-CBT), Narrative Exposure Therapy (NET) and Integrative Gestalt Therapy according to Dr. Hilarion Petzold (EAG-FPI) and validated it in a group of approximately 12 children from war and conflict zones who attend the public schools of the city of Zürich. Results: Qualitative feedback received from the teachers is promising. They report that it is now better possible for the children who go to ouer "Trauma group therapy with karate-do for war-traumatized children and adolescents" to concentrate at school and also to better regulate their feelings. Conclusion: Ouer approach seems to be a promising intervention for traumatized children and adolecents. Though it needs further evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".