Keeping the Horse Before the Cart: The Importance of Stabilization in Emotionally Focused Individual Therapy (EFIT)
Bibliographic record
Abstract
Our goal in this paper is to highlight the significance of Stage 1 stabilization processes in Emotionally Focused Individual Therapy (EFIT). Stabilization is a prerequisite to the modification of working models of self and other that typifies Stage 2 of EFIT, and we propose that it also has significant stand-alone value as a change event. We begin by defining the process of stabilization and delineating how it is a foundational part of the overall Emotionally Focused Therapy (EFT) model of change. Then we illustrate the autonomous value of the stabilization change event and show how stabilization is related to outcome in theory and in practice. Third, we demonstrate the use of specific interventions, the macro-moves and micro-skills of EFT, for achieving stabilization in an extended therapist-client dialogue of client change through the four steps of stabilization. Finally, we discuss the potential for therapists to overlook stabilization and the disadvantages of doing so.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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 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".