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Record W4410042810 · doi:10.1080/10503307.2025.2495773

Development and psychometric evaluation of a screening instrument for cognitive perceptual disruption (Copeds) in psychotherapy patients

2025· article· en· W4410042810 on OpenAlexaff
Mikkel Eielsen, Pål Ulvenes, Linne Melsom, Bruce E. Wampold, Jan Ivar Røssberg, Filip Myhre, Øystein Sørensen, Susanna Memmen Rasch, Allan Abbass

Bibliographic record

VenuePsychotherapy Research · 2025
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsDalhousie University
FundersModum Bad
KeywordsPsychotherapistPsychologyPerceptionCognitionClinical psychologyPsychiatryNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: Cognitive perceptual disruption (CPD) is an emotion regulation mechanism described in intensive short-term dynamic psychotherapy (ISTDP) that distinguishes fragile patients from resistant ones. CPD functions as an involuntary avoidance-based mechanism that prevents engagement with distressing emotions, making its identification crucial for guiding treatment. Currently, CPD assessment relies solely on clinical psychodiagnostic evaluation, with no available psychometric instruments. This study introduces a cognitive perceptual disruption screening instrument (Copeds), a self-report measure designed to identify CPD and assist in treatment planning for emotion-focused therapies. METHOD: A cross-sectional study was conducted to develop Copeds, evaluate its ability to distinguish between fragile and resistant patients, and examine its preliminary psychometric properties. 112 outpatients underwent clinical psychodiagnostic evaluation and completed Copeds. Regularized regression techniques, receiver operating characteristic (ROC) analysis, and analyses of internal consistency and sensitivity/specificity were performed. RESULTS: and specificity was 76.4%. CONCLUSION: The study provides promising initial evidence for Copeds as a reliable instrument with strong classification accuracy, supporting its potential use in clinical assessment and treatment planning for emotion-focused therapies.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.229
GPT teacher head0.501
Teacher spread0.272 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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