Group dynamic-relational therapy for perfectionism
Bibliographic record
Abstract
The interest in treating underlying core vulnerability factors or transdiagnostic processes has been a focus of much attention. In this paper we describe our application of group dynamic-relational psychotherapy to the treatment of perfectionism, a core personality vulnerability factor associated with various forms and types of dysfunction and disorders that have profound costs to the individual both socially and subjectively. Over the course of the past three decades, we developed an evidence-based integrative group treatment that targets the psychodynamic and relational underpinnings of perfectionism. The treatment is based on an integration of psychodynamic and interpersonal perspectives and therapeutic approaches. In this paper we present our model of perfectionism and describe our group dynamic-relational therapy for the treatment of its pernicious outcomes. By drawing on illustrative case material, we describe the approach as applied to one such group as it progresses through four phases of group development that we have termed engagement and pseudo attachment, pattern interruption, self-redefinition/painful authenticity, and termination. Finally, we present some of the accumulating evidence of the effectiveness and efficacy of dynamic-relational therapy.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".