The EMDR DeTUR protocol for the treatment of self-injury in a patient with severe personality disorder: a case report
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe the Eye movement desensitisation and reprocessing (EMDR) treatment of an adult female patient detained within a high secure hospital with complex mental health difficulties, including complex trauma, factitious disorder, self-injury and a history of offending. The EMDR treatment addressed the patient’s urges to engage in severe and sometimes life-threatening self-injury, a primary motive of which was to access physical healthcare interventions within a general hospital. The paper describes the wide-ranging benefits of the treatment and incorporates feedback from the patient and clinicians within her multi-disciplinary team (MDT). Design/methodology/approach Four triggers for self-injury were processed during the therapy using the DeTUR Protocol (Popky, 2005, 2009) and the Constant Installation of Present Orientation and Safety (CIPOS, Knipe, 2009a) method. In total, 18 one hour therapy sessions were delivered plus three follow-up sessions to continue to offer support and complete the post-treatment evaluation. Findings The level of urge for each trigger was reduced to 0 which the patient defined as “no urge to self-injure”. Benefits went well beyond self-injury with reported positive impacts on mood, thinking, sleep, concentration, memory and experience of flashbacks. Practical implications This case report demonstrates that the EMDR DeTUR Protocol together with the CIPOS method can be extremely valuable in the treatment of patients who self-injure. Originality/value The case report offers an important contribution to an area that requires much further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".