Bibliographic record
Abstract
Severe trauma such as the war combat, being taken as a hostage, brutal or repeated rape, affects all structures of the victim’s personality—one’s image of the body and sense of self; and one’s values and ideals—and leads to a sense that coherence and continuity of the self is systematically broken down. Severe trauma overwhelms the ordinary human adaptation and resistance as it usually involves the threat to life or bodily integrity and confronts the victim with the extremities of the helplessness, hopelessness, and terror, and evokes the response of catastrophe. In this paper, we describe how effectively the complex trauma is treated using the Dynamic Therapy model. We recognised five major alterations of the self as the aftermaths of severe trauma that should be targeted during treatment: (a) regulation of affected impulses; (b) attention and consciousness; (c) self-perception; (d) perception of the perpetrator; and (e) relation to others. The Dynamic Therapy model is the three-phase oriented treatment which applies to holotropic integration of the distorted self into a whole: (a) Impulse containment; Engagement; Safety; (b) Understanding; Recalling traumatic memories; (c) Self-conception; Enhancing daily living; Relapse prevention; Independency; Steps forward. The main concept of Dynamic Therapy model includes three treatment goals: (a) restoration of a form of the relatedness ( “Interconnectivity” ); (b) restoration of a sense of the aliveness/vitality ( “Dynamism” ); and (c) restoration of an awareness of self and inner events ( “Insight” ).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".