I don’t deserve anything good: Perfectionistic self-recrimination in a case of comorbid personality and eating disorder.
Bibliographic record
Abstract
In this case study we present the course of the psychotherapy of Myriam, a 19-year old female with a severe personality disorder and comorbid eating disorder. During the initial assessment she reported high levels of neuroticism that parallel the diagnosis of obsessive-compulsive personality disorder, borderline personality disorder, and anorexia nervosa. Myriam showed a severely impaired personality functioning defined by perfectionism, self-criticism, interpersonal guilt and overcontrol. Her daily experience was shaped by a self-recriminative inner dialogue associated with maladaptive patterns in the form of food, water and sleep restrictions, self-harm behaviors, and suicidal ideation. She accessed an integrative treatment based on individual (Evolutionary Systems Therapy) and group psychotherapy (Mindful Compassion for Perfectionism). At the end of 14-month intervention she remitted from all the categorical diagnoses and showed reliable changes in several measures. These outcomes were maintained at 3-month follow-up. We describe the integrative conceptualization based on Myriam’s perfectionistic self-recrimination patterns, and the consequent treatment that targeted these patterns rather than focusing on symptom reduction exclusively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".