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Record W3143216795 · doi:10.1521/pdps.2020.48.4.407

Money Questions and the Structural Interview

2020· article· en· W3143216795 on OpenAlexaff
Richard G. Hersh

Bibliographic record

VenuePsychodynamic Psychiatry · 2020
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsColumbia College
Fundersnot available
KeywordsContext (archaeology)PersonalityPsychologyPersonality pathologyCuriosityDimension (graph theory)Personality disordersPsychotherapistClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

Kernberg's structural interview integrates exploration of elements of personality organization into the standard initial psychiatric evaluation. The structural interview approach, while essential to transference-focused psychotherapy for patients with borderline personality disorder, is not limited to use in that context. Following the model of the structural interview, clinicians ask a series of questions, which elucidate elements of personality pathology, thereby facilitating comprehensive diagnosis, guiding treatment, and informing prognosis. Direct questioning about finances and the clinician's general curiosity about issues related to money, in the context of the structural interview, can be high-yield lines of inquiry. Patients' history with their finances, attitudes about money, and ways questions about finance emerge in the transference around fees and related concerns, can add an important, often overlooked, dimension to the assessment of personality organization and personality disorder pathology. This article proposes the utility of prioritizing questions regarding money, as might be integrated into the structural interview, as a template for a broader recognition of the value of this line of inquiry in a diagnostic assessment process.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.310
Teacher spread0.290 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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