Building Emotional Self-awareness Teletherapy in Civilians and Service Members With Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: To explore the early efficacy of building emotional self-awareness teletherapy (BEST) at reducing alexithymia and improving emotional functioning in civilians and military service members and veterans with mild traumatic brain injury (TBI). DESIGN: Nonrandomized, pretest-posttest design and a 3-month follow-up. SETTING: Community. PARTICIPANTS: Forty participants with mild TBI (20 civilians and 20 service members and veterans) and elevated alexithymia and emotion dysregulation. On average, participants were 40 years old and 13 years post-TBI. INTERVENTION: BEST is an 8-session, remotely delivered intervention that trains emotional insight. MAIN OUTCOME MEASURES: Toronto Alexithymia Scale-20, Levels of Emotional Awareness Scale, Difficulty with Emotion Regulation Scale, Brief Resilience Scale, Patient-Reported Outcomes Measurement Information System (PROMIS) Anxiety and Anger, Patient Health Questionnaire-9 (depression), Posttraumatic stress disorder Checklist for DSM-5 (PCL-5), Positive and Negative Affect Schedule, and Patient Global Impression of Change. RESULTS: Thirty-six participants completed the study (90% retention). Compared with baseline, participants had significant improvements immediately and 3 months after treatment on the Toronto Alexithymia Scale-20, Levels of Emotional Awareness Scale, Difficulty with Emotion Regulation Scale, Brief Resilience Scale, Positive and Negative Affect Schedule negative affect scale, PROMIS Anxiety and Anger, Patient Health Questionnaire-9, and PCL-5. All P values were <.001, except Positive and Negative Affect Schedule negative affect at immediate posttest (P=.001), PROMIS Anxiety at both posttreatment time points (P=.009 and P=.006, respectively), and PROMIS anger at 3-month follow-up (P=.001). At posttest, 75%, 70%, and 60% of participants improved by ≥½ SD on the Toronto Alexithymia Scale-20, Levels of Emotional Awareness Scale, and Difficulty with Emotion Regulation Scale, respectively. On the Patient Global Impression of Change, 83% of participants reported noticeable positive changes in their emotional functioning. CONCLUSIONS: Findings support the preliminary efficacy of BEST at improving psychological health in civilian and military participants with mild TBI who have elevated alexithymia and emotion dysregulation. However, larger trials with more rigorous designs are necessary to determine the true impact of BEST.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".