Development and psychometric evaluation of a screening instrument for cognitive perceptual disruption (Copeds) in psychotherapy patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".