Narrative flexibility in brief psychotherapy for depression
Bibliographic record
Abstract
OBJECTIVE: This study aimed to further understand how narrative flexibility contributes to therapeutic outcome in brief psychotherapy for depression utilizing the Narrative-Emotion Process Coding System (NEPCS), an observational measure that identifies specific markers of narrative and emotion integration in therapy sessions. METHOD: The present study investigated narrative flexibility by examining the contribution of NEPCS shifting (i.e., movement between NEPCS markers) in early, middle, and late sessions of client-centred therapy (CCT), emotion-focused therapy (EFT), and cognitive therapy (CT) and treatment outcome (recovered versus unchanged at the therapy termination). A logistic regression, with Wald tests of parameter estimates and pairwise comparisons, was used to test the study hypotheses. RESULTS: Results demonstrated that for recovered clients, the probability of shifting over the course of a therapy session was constant, whereas the probability of shifting declined for unchanged clients as the session progressed. There was also evidence that longer duration of time spent in any single NEPCS marker was negatively associated with shifting for both recovered and unchanged clients, although the effect was stronger for unchanged clients. CONCLUSIONS: The results provided preliminary support for the contribution of narrative flexibility to treatment outcomes in EFT, CCT, and CT treatments of depression.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".