Client subgroups in emotion‐focussed therapy: Exploring profiles in observable emotion
Bibliographic record
Abstract
Abstract Objective The present study examined client profiles in observed emotion to explore possible subgroups among clients given that there is substantial heterogeneity in clients' expression of emotion in therapy. Subgroups were identified using the sequential model of emotional processing, which posits that global distress, shame/fear and rejecting anger represent ‘early expressions of distress’, whereas assertive anger, self‐compassion and grief/hurt are conceptualised to be ‘adaptive states’ that predict good outcomes. The present study also offers a unique strategy for studying differences in clients' emotional presentation with implications for within‐session assessment by therapists. Method Sections of videotaped therapy sessions for 34 participants in emotion‐focussed therapy were coded for each emotional state. Through cluster analysis, participants were grouped based on the relative magnitude of each emotion state in the model. Results In the first set of analyses, two naturalistic groups were formed: Cluster 2 had higher levels of adaptive emotion and rejecting anger than Cluster 1. There was a significantly greater proportion of good within‐session outcomes in Cluster 2 than in Cluster 1. In the second set of analyses, a separate cluster analysis categorised participants based on their early expressions of distress. Four groups were described as follows: Distressed, Protesters, Fearful & Ashamed and Minimally Distressed. Only the Minimally Distressed group showed significantly higher rates of good within‐session outcome. Discussion These findings provide support for the emotional processing model by using novel methods and analyses. Although the findings are preliminary, they have implications for clinicians' assessments of clients' emotional needs and characteristic presentations.
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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 | 0.003 | 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) | 0.001 | 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".