Depth of experiencing as a client prognostic variable in emotion-focused therapy for adult survivors of childhood abuse.
Bibliographic record
Abstract
The term "experiencing" has been used to describe the quality of clients' engagement with their own internal experience (e.g., thoughts, feelings, images) during therapy. The present study investigated the relationship between depth of experiencing as a client characteristic and outcomes in Emotion-Focused Therapy for Adult Survivors of Child Abuse (EFT-AS). The study utilized archival data (audiotaped treatment sessions and self-report outcome measures) collected from 37 EFT-AS clients, who were dealing with issues of past emotional, physical, or sexual childhood abuse (Paivio & Nieuwenhuis, 2001). Clients' depth of experiencing was rated early in the therapy process during clients' discussion of core issues related to past abuse. Selected segments were transcribed and then rated using the Experiencing Scale (EXP; Klein, Mathieu-Coughlan, & Kiesler, 1986). A series of hierarchical multiple regressions were used to determine whether deeper levels of experiencing, early in therapy, predicted therapeutic outcomes. The results revealed that modal EXP ratings independently contributed to less symptomatology, reduced trauma symptoms, reduced interpersonal problems, and improved resolution of abuse issues. Peak EXP ratings did not significantly predict therapeutic outcomes. Together, the results suggest that experiencing can be used as a prognostic measure in EFT-AS and that maintaining a high EXP level seems to be more important to therapeutic outcomes than briefly attaining a high EXP level.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .R62. Source: Masters Abstracts International, Volume: 43-05, page: 1819. Adviser: Sandra Paivio. Thesis (M.A.)--University of Windsor (Canada), 2004.
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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".