Assessment of self‐contempt in psychotherapy: A neurobehavioural perspective
Bibliographic record
Abstract
Abstract Objective The aim of this methodological paper was to present self‐contempt, and its assessment, in a broad transdiagnostic framework of psychopathology and related to change in psychotherapy. Self‐contempt may be a central phenomenon in many psychological disorders. We outline methodological recommendations for the study of complex transdiagnostic phenomena which involve multilevel biobehavioural responses. Method We illustrate the assessment of self‐contempt as a complex transdiagnostic phenomenon with a study in which emotion‐eliciting two‐chair dialogues focused on the elaboration of self‐criticism, and an observer‐rated system was applied to assess each client's expressed self‐contempt at the moment of enacted self‐criticism. The client's own self‐contemptuous words were extracted from this self‐critical dialogue and then later presented as part of a functional magnetic resonance imaging paradigm. Results This assessment paradigm was applied to a brief treatment for clients with borderline personality disorder, and the results of pre–post change over time in markers of self‐contempt are presented. Conclusions The importance of the assessment of self‐contempt in an ecologically valid manner, by using individualised stimuli and taking into account multilevel activations, is discussed in the context of a transdiagnostic conception of psychopathology and in the context of change in psychotherapy.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".