Conversation analysis of the two-chair self-soothing task in emotion-focused therapy
Bibliographic record
Abstract
OBJECTIVE: Despite an increasing recognition of the relevance and significance of self-compassion processes, little research has explored interventions that seek to enhance these in therapy. In this study, we examined the compassionate self-soothing task of emotion-focused therapy involving two-chair work, with seven clients. METHOD: Conversation analysis was used to examine client-therapst interaction. RESULTS: The analysis yielded a detailed description of interactional practices and processes involved in the accomplishment of self-soothing, drawing on Goffman's concept of the participation frame. We show how therapists and clients collaborate to move from the ordinary frame of therapeutic conversation to a self-soothing frame and back again by using various interactional practices: Therapists' instructions to clients, specific ways of sequencing actions in interaction, explanations and justification of the importance of the self-soothing task, pronouns as a way to distinguish among addressees (e.g., clients versus soothing agents), corrections of clients' talk, and response tokens (hm mm, yeah, good). These practices are used to help clients accomplish self-soothing in the form of self-praise, disclosing caring, and offering of helpful advice. CONCLUSIONS: This study offers therapists a specific account of how to respond to clients at specific junctures in self-soothing dialogues and how to structure and accomplish the self-soothing task.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".