Passing tests and using one's attitude to help patients overcome their pathogenic feelings of guilt and shame
Bibliographic record
Abstract
Guilt and shame are emotions that, albeit subjectively negative, help humans adapt to their social environment. However, in some cases, there are pathogenic beliefs, shaped over the lifespan that sustain them and make them a source of psychopathological suffering. In this paper we will first briefly show how Control-Mastery Theory (CMT) considers several types of pathogenic beliefs shaped by traumatic experiences that underly chronic feelings of guilt and shame. We then focus on a clinical case of Livia, a 28 year-old woman with relational and academic problems suffering mainly from two such types of pathogenic beliefs: burdening guilt and disloyalty guilt. We describe how a) Livia was driven by adverse and traumatic experiences to form some of these pathogenic beliefs, b) how she tested the therapist in order to discover whether he would disconfirm these beliefs, and c) how the therapist was able to successfully pass these tests and provide her with new and healthier interpersonal experiences. The case of Livia will highlight therapists' ability to accurately formulate patients' goals, pathogenic beliefs-including types of guilt- and shame-related beliefs-and traumas. Moreover, the case will illustrate how therapists can pass patients' tests and adopt the right attitude to help patients disprove their pathogenic beliefs and overcome problematic experiences of guilt and shame.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".