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Record W2166564387 · doi:10.1177/070674370505000803

Principles and Strategies for Treating Personality Disorder

2005· review· en· W2166564387 on OpenAlexaffvenue
W. John Livesley

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2005
Typereview
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychological interventionPsychotherapistPsychodynamicsInterpersonal communicationPsychologyPersonality disordersCognitionIntervention (counseling)PersonalityClinical psychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

This paper proposes a systematic framework for treating personality disorder, based on research on the nature and origins of the disorder and treatment outcome. It adopts an eclectic approach that combines interventions from different therapeutic models and delivers them in an integrated and systematic manner. Coordination of multiple interventions is achieved by emphasizing the nonspecific component of therapy, especially the treatment frame and generic interventions. Specific interventions drawn from different treatment models, including medication, are built onto this foundation as needed to tailor treatment to the individual. Coordination and integration are also achieved by conceptualizing treatment as progressing through a series of phases, each addressing different problems with different specific interventions. Five phases are described: safety, containment, regulation and control, exploration and change, and integration and synthesis. During the earlier phases, structured behavioural and cognitive interventions and medication predominate. Later in treatment, these interventions are supplemented with less structured psychodynamic, interpersonal, and constructionist strategies to explore and change maladaptive interpersonal patterns, cognitions, and traits and to forge a more integrated and adaptive self-structure or identity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.073
GPT teacher head0.371
Teacher spread0.298 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations59
Published2005
Admission routes2
Has abstractyes

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