Into the Wild, Out of the Woods: A Systematic Case Study on Facilitating Emotional Change
Bibliographic record
Abstract
Cognitive and behavioral treatment programs for individuals who have committed sexual offenses (ISOs) have shown significant but small effect sizes. A growing body of research points toward the importance of difficulties in affect regulation (AR) as a risk factor for sexual recidivism. On this basis, it seems important to target difficulties in AR in treatment. The current systematic case study investigates the potential contribution of emotion-focused therapy (EFT) to changing problematic AR in ISOs. Kevin was a high-risk offender with a traumatic history who met the diagnostic criteria of pedophilic and borderline disorders, with serious AR difficulties. Self-report outcome measures, observation measures, and a biomarker were used to track changes in AR, psychological symptoms, and distress during baseline (Phase A); treatment as usual (Phase B); treatment with an EFT component added (Phase C); and follow-up (Phase A). Statistically significant change was found in AR, psychological symptoms, and distress during treatment (Phase B + C); however, it is not possible to attribute these changes causally to EFT. An examination of the qualitative process data provides deeper insights into how the client reacted to specific EFT interventions. Verbatim clinical vignettes are included to clarify key interventions, hindrances, and mechanisms of change. This study provides preliminary support for the role of therapy to facilitate emotional change in ISOs.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".