MétaCan
Menu
Back to cohort
Record W2616720899 · doi:10.1080/14999013.2017.1315469

The Potential for a Skills-Based Dialectical Behavior Therapy Program to Reduce Aggression, Anger, and Hostility in a Canadian Forensic Psychiatric Sample: A Pilot Study

2017· article· en· W2616720899 on OpenAlexaffabout
Monica F. Tomlinson, Peter N. S. Hoaken

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2017
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsWestern University
FundersSouthwest HospitalDepartment of Biotechnology, Ministry of Science and Technology, India
KeywordsHostilityAggressionAngerDialectical behavior therapyPsychologyClinical psychologyPsychiatryBorderline personality disorder

Abstract

fetched live from OpenAlex

Dialectical behavior therapy (DBT) is designed to target maladaptive behaviors, such as aggression. The present pilot study assessed whether DBT reduces aggression, anger, and hostility in a forensic psychiatric sample ( N = 15). A randomized waitlist control pre-post/follow-up crossover design was employed. Group-level findings indicated a reduction in hostility during and following DBT. Individual-level findings indicated some reductions in aggression during and following DBT. Individual-level findings indicated that participants in DBT improved more on measures of aggression compared to participants in treatment as usual. These results are preliminary. Implications and future directions in research are discussed.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.511
Threshold uncertainty score0.973

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.440
Teacher spread0.397 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Forensic Mental HealthSame topicPersonality Disorders and PsychopathologyFrench-language works237,207