Responding to self-criticism in psychotherapy
Bibliographic record
Abstract
OBJECTIVE: We explored the interactive process in which therapists respond to client self-critical positions. METHODS: Drawing from the resources of conversation analysis (CA), we examined a corpus of in-session self-critical sequences of talk occurring in different kinds of treatments: Client Centered Therapy, (CCT), Emotion Focused Therapy (EFT), Psychoanalytic Psychotherapy (PP) and in different cultural contexts. RESULTS: It was found that client self-critical talk performed various functions pertaining to diminished control, accountability (e.g., failed obligations leading to self-blame) and disparaging evaluations of self (contempt or disgust). Further, therapists were found to respond in ways that targeted the client's report of having diminished control or of being accountable for their negative attributes by providing a more optimistic reading of the client's experience, one that is more open to positive outcomes and the possibility of change. Our sequential analysis not only shows how clients may resist these optimistic readings, but also how therapists work towards successfully achieving moments of re-affiliation. CONCLUSION: We anticipate that the fine-grained sequential analysis of therapy interaction can provide therapists with a more detailed understanding of the options and challenges therapists face when working with clinical challenges of clients' self-critical positions.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".