Emotion in an alliance rupture and resolution sequence: A theory‐building case study
Bibliographic record
Abstract
Aims: Alliance rupture and resolution processes are occasions for the client to have his or her core interpersonal patterns activated in the here and now of the therapy and to negotiate them with the therapist. So far, no studies have been conducted on emotional processing, from a sequential perspective using distinct emotion categories, in alliance rupture and resolution therapy sessions. This is the objective of this theory‐building case study. Method: This client underwent a 34‐session long, psychodynamic psychotherapy within the context of an open trial. An alliance rupture‐resolution sequence of two subsequent sessions, along with a third control session, was selected from this case and these sessions were rated using the Classification of Affective‐Meaning States (CAMS), an observer‐rated method to classify distinct emotions, according to current emotion‐focused models. Results: The results indicate that the rupture session was associated, above all, with core maladaptive fear, evoked in the actual here and now of the therapeutic relationship, whereas the resolution session was associated with the expression and experience of adaptive hurt as regards biographical issues of the client. Discussion: These results are discussed with regard to the alliance rupture and resolution model and the exploration of integrating client's emotional processing in the model.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".