A Review of Empirical Studies Investigating Narrative, Emotion and Meaning-Making Modes and Client Process Markers in Psychotherapy
Bibliographic record
Abstract
Abstract Despite the importance of narrative, emotional and meaning-making processes in psychotherapy, there has been no review of studies using the main instruments developed to address these processes. The objective is to review the studies about client narrative and narrative-emotional processes in psychotherapy that used the Narrative Process Coding System or the Narrative-Emotion Process Coding System (1.0 and 2.0). To identify the studies, we searched The Book Collection, PsycINFO, PsycARTICLES, PsycBOOKS, PEP Archive, Psychology and Behavioral Sciences Collection, Academic Search Complete and the Web of Knowledge databases. We found 27 empirical studies using one of the three coding systems. The studies applied the Narrative Process Coding System and the Narrative-Emotion Process Coding System to different therapeutic modalities and patients with various clinical disorders. In some studies, early, middle and late phases of therapy were compared, while other studies conducted intensive case analyses of Narrative Process Coding System and Narrative-Emotion Process Coding System patterns comparing recovered vs unchanged clients. The review supports the importance to look for the contribution of narrative, emotion, meaning-making patterns or narrative-emotion markers, to treatment outcomes and encourages the application of these instruments in process-outcome research in psychotherapy.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".