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Record W3192708884 · doi:10.3138/jmvfh-2019-0067

Effects of a modified DBT Skills Group for military personnel and Veterans with OSIs and borderline personality disorder or traits

2021· article· en· W3192708884 on OpenAlexaffvenue
Pamela L. Holens, Jeremiah N. Buhler, Stephanie Yacucha, Alyssa Romaniuk, Brent Joyal

Bibliographic record

VenueJournal of Military Veteran and Family Health · 2021
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBorderline personality disorderClinical psychologyPsychiatryPsychologyMental healthFeelingIntervention (counseling)Military personnelPersonalityDepression (economics)Dialectical behavior therapy

Abstract

fetched live from OpenAlex

LAY SUMMARY This study looked at the use of a group treatment known as Dialectical Behaviour Therapy Skills Group (DBT-SG) to see if it was helpful for military personnel and Veterans who had a variety of mental health disorders related to their service. The results of the study showed improvements in symptoms of borderline personality disorder, reductions in negative thoughts and feelings, and reductions in unhelpful behaviours. Results also showed improvements in all examined areas of functioning among participants, with the largest change occurring in the area of social functioning. The presence of posttraumatic stress disorder (PTSD), depression, or chronic pain did not impact results, but the presence of a substance abuse disorder did. Overall, the results provide preliminary support for DBT-SG as an intervention for borderline personality disorder symptoms among military and Veterans, and perhaps particularly for persons who also have other mental health challenges, or persons for whom other treatment may be considered inappropriate.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.023
GPT teacher head0.313
Teacher spread0.291 · 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 designNon-randomized trial
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

Citations1
Published2021
Admission routes2
Has abstractyes

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