Resolving Social Inhibition During Emotion-Focused Therapy for Depression: A Task Analytic Discovery
Bibliographic record
Abstract
The aim of this study was to create a model of the resolution of social inhibition (SI) during emotion-focused therapy (EFT; Greenberg et al., 1993) for depression. Employing the steps of the discovery phase of a task analysis (Greenberg, 2007), a rational model of the resolution of SI was first developed. Client markers of SI were also conjectured. Following this, performances of the resolution and non-resolution of SI over a course of EFT therapy for depression were observed, using archival data of six clients from clinical trials of EFT for depression (Greenberg & Watson, 1998; Goldman et al., 2006). Resolution was defined as having an SI score on the Inventory of Interpersonal Problems (Horowitz et al., 1988) in the normal range, as indicated by norms, at 18-month follow-up post therapy. The empirical observations were then synthetized with the rational model to create a final rational-empirical model outlining the resolution of SI. The final model identified 6 components: (1) SI Markers; (2) Maladaptive shame and fear expressed by the client’s inhibited self; (3) Client connects SI Agent to painful past original source; (4) A power shift that results in an overcoming of the part of client that perpetuates SI (through expression of assertive anger and hurt/grief, needs for support and acceptance, and deservingness of needs); (5) Client is willing to take risks despite potential hurt/grief; and (6) Increased expression of self-assertion. Theoretical and clinical implications of the findings are considered. Limitations and future research directions are discussed.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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 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".