Reductions in Alexithymia and Emotion Dysregulation After Training Emotional Self-Awareness Following Traumatic Brain Injury: A Phase I Trial
Bibliographic record
Abstract
OBJECTIVES: To examine the acceptability and initial efficacy of an emotional self-awareness treatment at reducing alexithymia and emotion dysregulation in participants with traumatic brain injury (TBI). SETTING: An outpatient rehabilitation hospital. PARTICIPANTS: Seventeen adults with moderate to severe TBI and alexithymia. Time postinjury ranged 1 to 33 years. DESIGN: Within subject design, with 3 assessment times: baseline, posttest, and 2-month follow-up. INTERVENTION: Eight lessons incorporated psychoeducational information and skill-building exercises teaching emotional vocabulary, labeling, and differentiating self-emotions; interoceptive awareness; and distinguishing emotions from thoughts, actions, and sensations. MEASURES: Toronto Alexithymia Scale-20 (TAS-20); Levels of Emotional Awareness Scale (LEAS); Trait Anxiety Inventory (TAI); Patient Health Questionnaire-9 (PHQ-9); State-Trait Anger Expression Inventory (STAXI); Difficulty With Emotion Regulation Scale (DERS); and Positive and Negative Affect Scale (PANAS). RESULTS: Thirteen participants completed the treatment. Repeated-measures analysis of variance revealed changes on the TAS-20 (P = .003), LEAS (P < .001), TAI (P = .014), STAXI (P = .015), DERS (P = .020), and positive affect (P < .005). Paired t tests indicated significant baseline to posttest improvements on these measures. Gains were maintained at follow-up for the TAS, LEAS, and positive affect. Treatment satisfaction was high. CONCLUSION: This is the first study published on treating alexithymia post-TBI. Positive changes were identified for emotional self-awareness and emotion regulation; some changes were maintained several months posttreatment. Findings justify advancing to the next investigational phase for this novel intervention.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".