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Record W2755930302 · doi:10.1521/pedi_2017_31_307

Substance Misuse Is Associated With Increased Psychiatric Severity Among Treatment-Seeking Individuals With Borderline Personality Disorder

2017· article· en· W2755930302 on OpenAlexafffund
Laura M. Heath, Lise Laporte, Joel Paris, Kevin Hamdullahpur, Kathryn Gill

Bibliographic record

VenueJournal of Personality Disorders · 2017
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsPsychiatryBorderline personality disorderImpulsivityPsychologySubstance abuseClinical psychologyMoodDual diagnosisPsychological interventionAddictionComorbidity

Abstract

fetched live from OpenAlex

Despite high prevalence rates of concurrent borderline personality disorder (BPD) and substance use disorders (SUDs), little is known about the impact of substance misuse on the presentation of BPD. Sixty-five individuals with BPD were assessed at intake and at 3- and 6-month follow-up. Assessment included validated instruments such as the Addiction Severity Index and the Revised Symptom Checklist (SCL-90-R). Over half (58.5%) of individuals entering treatment were currently misusing substances. Substance misuse was associated with more legal and employment problems, greater mood disturbance, impulsivity, and psychiatric severity, including almost all SCL-90-R subscales. For the majority of patients (58%), there was little change in substance misuse post-treatment. The high prevalence of substance misuse and its association with psychiatric severity among individuals with BPD suggest that substance misuse should be a targeted behavior during treatment, and further specialized interventions are needed for individuals with comorbid BPD and SUD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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 teacher head, not a consensus.

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

Citations28
Published2017
Admission routes2
Has abstractyes

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