MétaCan
Menu
Back to cohort
Record W2199016635 · doi:10.1521/pedi.2015.29.4.526

A Lot Can Happen in a Few Minutes: Examining Dynamic Patterns Within an Interaction to Illuminate the Interpersonal Nature of Personality Disorders

2015· article· en· W2199016635 on OpenAlexaff
Pamela Sadler, Erik Z. Woody, Kelly McDonald, Ivana Lizdek, Jerrica Little

Bibliographic record

VenueJournal of Personality Disorders · 2015
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
Fundersnot available
KeywordsPsychologyInterpersonal communicationModerationPersonalityInterpersonal relationshipSocial psychologyAssociation (psychology)Dominance (genetics)Social relationDevelopmental psychologyCognitive psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Although problematic interpersonal tendencies have often been characterized as a traitlike excess of a particular interpersonal style, the interpersonal nature of personality disorders may have more to do with patterns of variability in interpersonal behavior and the relation of this variability to the varying behavior of interaction partners. Indeed, problematic interpersonal tendencies may often be evident as patterns within even one interaction. A useful methodology for examining moment-to-moment patterns within the course of an interaction is the computer joystick technique. To illustrate the potential of this new approach for studying problematic interpersonal patterns, the authors provide joystick-based analyses of the videoed session between Dr. Donald Meichenbaum and the client, Richard (Shostrom, 1986a). The authors show how to examine the association between concurrent levels of dominance and affiliation within a person, patterns of covariation between partners, and the moderation of such entrainment patterns. They also discuss how these indices could illuminate disordered interpersonal patterns.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.355
Teacher spread0.314 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Personality DisordersSame topicPersonality Disorders and PsychopathologyFrench-language works237,207