A close look at therapist contributions to narrative-emotion shifting in a case illustration of brief dynamic therapy
Bibliographic record
Abstract
Objective: In a secondary analysis of Friedlander et al.'s [(2018). “If those tears could talk, what would they say?” multi-method analysis of a corrective experience in brief dynamic therapy. Psychotherapy Research, 28, 217–234. doi:10.1080/10503307.2016.1184350] case study of Hanna Levenson’s Brief Dynamic Therapy over Time (from APA’s Psychotherapy in Six Sessions DVD series), we re-visited the Narrative-Emotion Process Coding (Angus, L. E., Boritz, T., Bryntwick, E., Carpenter, N., Macaulay, C., & Khattra, J. (2017). The Narrative-Emotion Process Coding System 2.0: A multi-methodological approach to identifying and assessing narrative-emotion process markers in psychotherapy. Psychotherapy Research, 27, 253–269. doi:10.1080/10503307.2016.1238525) to identify specific therapist behaviors that may have facilitated the client’s movement from expressing mostly Problem markers in early sessions to expressing considerably more Transition and Change markers in later sessions. Method: Using open coding and constant comparison qualitative methods, we identified Levenson’s behaviors immediately preceding the client’s “change shifts” (Problem → Transition/Change and Transition → Change) and “problem shifts” (Transition/Change → Problem). Results: Compared to problem shifts, change shifts were preceded by more therapist behavior reflecting Attaching New Meaning (e.g., linking the client’s self-deprecation to her avoidant behavior) and Exploring/Expanding emotions (e.g., inviting the client to give voice to her tears), cognitions (e.g., pointing out the client’s self-talk) and motivation (e.g., reflecting on the client’s dissatisfaction with her defenses). Conclusions: In this successful case, facilitative therapist behavior reflected common therapeutic responses (e.g., validating the client’s perspective) as well as responses characteristic of brief dynamic therapy (e.g., interpreting the client’s defenses) and the therapist’s personal style (e.g., repeating the client’s words for emphasis).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".