Clients’ Depth of Experiencing and Narrative-Emotion Processes in Psychotherapy
Bibliographic record
Abstract
Studies of narrative-emotive processes and clients’ depth of experiencing in psychotherapy have gained prominence over the past decades. While their process rating measures have been shown to be associated with positive treatment outcomes, no studies to date have compared them. To address this gap, the current methodological study sought to test the relationship between them: narrative-emotive processes were measured by the Narrative-Emotion Process Coding System (NEPCS) 2.0 and experiencing processes by the Experiencing scale (EXP scale) in a sample of 40 video-recorded sessions of highly expert therapists selected from seven brief psychotherapy modalities. Multilevel regression analyses were performed to capture the relationship between the problem markers, transition markers, change markers, and the depth of experiencing. As hypothesized, we found that the frequency of the three NEPCS subgroups significantly and positively predicted the seven EXP levels. The qualitative analysis showed substantial variability between therapists in EXP frequencies and a higher frequency of transition markers, followed by the problem and change markers. Both measures captured how change occurs. However, while the EXP focused on emotional awareness, the NEPCS was broader and captured the degree of narrative-emotional integration. The EXP scale could be more sensitive in identifying variability between therapists than the NEPCS scale.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".