Developing a Systematic Procedure for the Assessment of Self-Defining Memories in Psychodynamic Therapy: Promise and Pitfalls
Bibliographic record
Abstract
The innovative approach to assessing autobiographical memory narratives that Singer and Bonalume (2010) demonstrate in their case study of Cynthia is an ambitious expression of integrative psychotherapy research. It brings together the rich research findings on self-defining memories derived from laboratory studies and therapy case analyses, and applies these to the multimodal assessment situation in a psychotherapy program. Further, Singer and Bonalume's case of Cynthia is grounded in a truly "common factor" that is essential to most if not all psychotherapies: patient narrative expression. However, the integration of findings across different research studies still needs further elaboration to clarify and explore when they are consistent and when they are inconsistent with one another. In our commentary we critically assess the following issues associated with Singer and Bonalume's narrative memory coding system and its application to the case of Cynthia: (a) the utilization of narrative analyses for the identification of themes; (b) challenges inherent in establishing criteria for the identification of clinically important autobiographical memory narratives in therapy sessions; (c) the degree of integrative processing that takes place in narrative expression; and (d) the process of formulating inferences based on client narrative expression in assessment interviews versus therapy sessions. The commentary concludes with a discussion of promising future directions for narrative research in psychotherapy.
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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 | 0.481 | 0.503 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".