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Record W4416993507 · doi:10.1016/j.apmr.2025.10.029

Building Emotional Self-awareness Teletherapy in Civilians and Service Members With Mild Traumatic Brain Injury

2025· article· en· W4416993507 on OpenAlexaboutno aff
Dawn Neumann, Treven C. Pickett, Jie Ren, Yuedi Yang, Flora Hammond

Bibliographic record

VenueArchives of Physical Medicine and Rehabilitation · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersCongressionally Directed Medical Research ProgramsU.S. Department of DefenseU.S. ArmyU.S. Army Medical Research and Development CommandDefense Health AgencyOffice of the Assistant Secretary for Health
KeywordsAlexithymiaTraumatic brain injuryService memberRehabilitationService (business)Military serviceMilitary personnel

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.026
GPT teacher head0.362
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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