Transforming core emotional pain in a course of emotion-focused therapy for depression: A case study
Bibliographic record
Abstract
OBJECTIVE: To investigate the pattern of change in emotional states over a course of emotion-focused therapy using the model of sequential emotional processing as an initial framework for analysis. METHOD: This was a single case study observational design examining 15 sessions of therapy with one client. A qualitative analysis of moment-to-moment shifts in client emotional events was conducted. This conceptualised the interplay between experienced emotions using the sequential emotional processing model as an interpretative framework. The analysis was triangulated by using existing observer-based rating scales and reliability assessed with an independent rater. RESULTS: The sequential emotional processing model was found to be an effective means to explain the sequence of expressed emotional events, although some emotional events and emotion scheme change processes pertaining to this particular case required additional explanation than provided in the original model descriptions. CONCLUSIONS: Observed nuances in this specific case included highlighting triggers to emotional experience and avoidance processes fuelled by anticipatory fear. The observations included a process of change through accessing core feelings of shame, fear, and loneliness and their transformation through the generation of self-compassion and assertive anger. Implications for practice are discussed in terms of case conceptualisation and therapeutic strategy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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 teacher head, 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".