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Record W4310700290 · doi:10.1521/pedi.2022.36.6.731

The Relationship Between Patients' Personality Traits, the Alliance, and Change in Interpersonal Distress in Intensive Group Treatment for Personality Dysfunction

2022· article· en· W4310700290 on OpenAlexaffabout
Katie Aafjes‐van Doorn, David Kealy, Johannes C. Ehrenthal, John S. Ogrodniczuk, Anthony S. Joyce, Rainer Weber

Bibliographic record

VenueJournal of Personality Disorders · 2022
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsPsychologyExtraversion and introversionDistressPersonalityClinical psychologyInterpersonal communicationPersonality Assessment InventoryMediationAlliancePersonality disordersBig Five personality traitsPsychotherapistPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

This study examined patients' personality traits as operationalized by the five-factor model in relation to early alliance and reduction of interpersonal distress through an intensive group treatment program for personality dysfunction. A sample of 79 consecutively admitted psychiatric outpatients with personality dysfunction who attended an 18-week intensive group treatment program completed the NEO Five-Factor Inventory at pretreatment, the Inventory of Interpersonal Problems at pre- and posttreatment, and the Edmonton Therapeutic Alliance Scale, a measure of the therapeutic alliance with the program therapist, at Session 5. Results indicated that patients who were relatively extraverted tended to rate the alliance with their program therapist higher and subsequently reported more improvement of interpersonal distress. The presence of a personality disorder did not moderate this mediation. Patients' extraversion likely promotes a bonding with the therapist and facilitates the interpersonal group work necessary for improvement. Assessing patients' level of extraversion before starting intensive group treatment might indicate which intervention strategies could be useful with that patient within the program frame.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.628

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.083
GPT teacher head0.368
Teacher spread0.286 · 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.

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

Citations2
Published2022
Admission routes2
Has abstractyes

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