Emotion cascade: Harnessing emotional sequences to enhance chair work interventions and reduce self-criticism
Bibliographic record
Abstract
OBJECTIVE: This study examines if experiencing the sequence of primary maladaptive emotions followed by primary adaptive emotions in-session predicts therapeutic change and whether this sequence mediates the impact of therapist emotional reflections on outcomes at post-treatment and follow-up. METHOD: Nineteen participants with high self-criticism underwent 10-12 sessions of emotion-focused therapy (EFT). Therapist responses focusing on emotions, thoughts, and actions were coded for two sessions (sessions 6-12) during the initial 10 minutes prior to chair work. Clients' emotional states were coded using the Classification of Affective Meaning States (CAMS) during the subsequent chair work. Self-criticism and depression were measured at pre-treatment, post-treatment, and 3-month follow-up. RESULTS: Primary maladaptive emotions and the transformational sequence (primary maladaptive followed by adaptive emotion) predicted reductions in self-criticism at post-treatment, with the transformational sequence also predicting improvements at follow-up. The impact of therapist focus on emotions on depression and self-criticism at post-treatment and follow-up was mediated by the transformational sequence. CONCLUSION: The transformational sequence predicts therapeutic outcomes and mediates the impact of therapist responses focused on the client's emotion and therapeutic results. Implications for therapist training are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".