Return to work program efficacy with Self-Regulation Therapy (SRT®): Case study with complex trauma and concurrent disorders
Bibliographic record
Abstract
Background This study shows the efficacy of treating complex cases neurobiologically using Self-Regulation Therapy (SRT®) within the context of return to work goals. Case presentation This is a single case study of a 32-year-old white female. This case study follows a client with concurrent diagnoses of post-traumatic stress disorder (PTSD), bipolar disorder I and substance abuse over the course of 2 years of treatment with SRT®. Using SRT® as primary modality and Likert Scale self-report on the Zettl Scale of Dysregulation, psychiatric medication monitoring and pharmaceutical tracking, this study shows session summaries and progress. Results After six sessions the client was cleared by her psychiatrist for return to work. Her medications were reduced and her post-traumatic symptoms abated. She no longer met diagnostic criteria for PTSD or substance abuse after nine sessions. She returned to work successfully and maintained sobriety and continued symptom reduction. Follow up over a 2-year time period showed consistency and continued improvements in both her professional and her personal life. Conclusions Clients with complex traumatic history with concurrent diagnosis are typically difficult to treat in traditional psychotherapy with limited long-term success. This creates challenges in therapy because the traumas occur during key developmental periods of life. This study shows the efficacy of treating complex cases neurobiologically using SRT®. Using SRT®, clinicians are able to address both developmental and complex trauma to reduce sympathetic arousal in the nervous system providing symptom reduction and even resolution of previous clinical diagnoses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 | 0.002 |
| 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".