Complex Trauma, Somatoform Dissociations & Energetics Therapy
Bibliographic record
Abstract
Introduction Mental health professionals find it very challenging to provide counselling and therapy when confronted with disclosures of ritual, satanic and extreme abuse. Psychometric and muscle testing can facilitate diagnosis and healing in this context. Psychiatrists of renown such as David Hawkins and Colin Ross have embraced and written about energetic medicine in their practice. Objectives The presentation explains how somatoform dissociations are tell-tale indications of abuse and neglect of early childhood trauma and how ‘Energetics’ therapy facilitates healing. Aims Delegates will learn to recognise somatoform dissociation symptoms, understand advances and limitations of psychometric assessment tools, appreciate energetics approaches as an adjunct to other intervention methods and gain an insight into the origins of complex trauma. Methods Two case studies are used to illustrate causes, impact, diagnosis and healing of complex trauma. Results A set of psychometric assessments helped to unravel a chilling revictimisation crime series. ‘Twice Exceptional’ characteristics were very high IQ coupled with Dyslexia, very weak auditory memory and psychic capabilities. In another case that stemmed from extreme abuse of ancient, commercial and high-tech varieties muscle testing and energetics therapy lead to a remarkable recovery. Conclusions Psychometric and muscle testing can inform diagnosis, therapy and healing. Energetics can be used to bring about profound healing for those who have repressed severe trauma. This method has many advantages in that parts of it are easily learned, it is non-invasive, has no side effects, gives patients control over their reactions, eliminates triggers and offers healing. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".