Clients’ Retrospective Accounts of Corrective Experiences in Psychotherapy: An International, Multisite Collaboration
Bibliographic record
Abstract
This article introduces a series of 4 original research reports that used varied qualitative methods for understanding an internationally diverse sample of clients' own accounts of corrective experiences (CEs), as they looked back on their completed psychotherapy. The basis for all studies, which were conducted across 4 different countries, was the Patients' Perceptions of Corrective Experiences in Individual Therapy (PPCEIT) semistructured interview protocol (Constantino, Angus, Friedlander, Messer, & Moertl, 2011). The PPCEIT interview assesses clients' retrospective accounts of aspects of self, other, and/or relationships that may have been corrected, and what they perceived as corrective experiences that facilitated such transformations. It also asks for specific, detailed examples of these accounts and experiences. Across all studies, the PPCEIT interview generated rich clinical material and resulting empirically generated themes that may inform clinical practice. After briefly defining the CE construct and highlighting a lack of research on clients' own accounts of such experiences, we describe the development of the PPCEIT interview (and provide the full interview manual and question protocol as appendices). We then summarize the foci of the culturally diverse reports in this series.
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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 | 0.032 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".