Making Your Emotions Work for You: A pilot brief intervention for alexithymia with personality‐disordered offenders
Bibliographic record
Abstract
ABSTRACT One strategy for reducing premature termination of therapy is to offer pre‐therapy preparation. Here, we describe an intervention targeting alexithymia in offenders with personality disorders. ‘Making Your Emotions Work for You’ is a one‐day group intervention consisting of four evidence‐based components: Psychoeducation, Recognizing Emotions, Self‐Awareness and Seeking Information. Pre‐intervention and post‐intervention scores on measures of alexithymia (Toronto Alexithymia Scale‐20 (TAS‐20)) and psychological mindedness (Balanced Index of Psychological Mindedness (BIPM)) are presented for five male personality‐disordered offenders. This small sample showed consistent scores in the dysfunctional direction on both psychometric measures, indicating that there does appear to be a need to address alexithymia in this group. Overall, participants reported positive experiences with the intervention. No reliable pre‐intervention to post‐intervention changes were observed on TAS‐20 scores. On the BIPM Interest scale, reliable improvement was observed for three participants and reliable deterioration for one participant. On the BIPM Insight scale, one participant reliably improved, and one reliably deteriorated. While alexithymia may not be changed by this intervention, participants may become more interested in exploring their emotions, which may enhance engagement in therapy. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".