Client Accounts of Corrective Experiences in Psychotherapy: Implications for Clinical Practice
Bibliographic record
Abstract
The Patient Perceptions of Corrective Experiences in Individual Therapy (PPCEIT; Constantino, Angus, Friedlander, Messer, & Moertl, 2011) posttreatment interview guide was developed to provide clinical researchers with an effective mode of inquiry to identify and further explore clients' firsthand accounts of corrective and transformative therapy experiences and their determinants. Not only do findings from the analysis of client corrective experience (CE) accounts help identify what and how CEs happen in or as a result of psychotherapy, but the measure itself may also provide therapists with an effective tool to further enhance clients' awareness, understanding, and integration of transformative change experiences. Accordingly, we discuss in this afterword to the series the implications for clinical practice arising from (a) the thematic analysis of client CE accounts, drawn from a range of clinical samples and international research programs and (b) the clinical effect of completing the PPCEIT posttreatment interview inquiry. We also identify directions for future clinical training and research.
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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 | 0.066 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".