Alexithymia and change process: Contributions of a phase model
Bibliographic record
Abstract
Abstract The research literature shows patients with alexithymia have poorer outcomes in psychotherapy. We reflect upon psychotherapy with alexithymic patients presenting one case study, from a larger research project, throughout 12 sessions. This study used a mixed‐methods design with qualitative and quantitative methods exploring both client's and therapist's perspectives. From the client's perspective, different dimensions were explored: emotional process components, symptom severity, therapeutic alliance and the processing capacity regarding the phase‐specific general strategies as postulated in the Paradigmatic Complementarity Metamodel (PCM). Regarding the therapist's perspective, we investigated the PCM phase‐specific strategic goals promoted throughout the process and the perception of the therapeutic alliance. Quantitative and qualitative data are presented. The client presented a significant change in both alexithymia and symptom severity regarding the reliable change index, being considered a good outcome. The sequencing of general strategies as a phase‐by‐phase map of the therapeutic process is used to describe the development of client's and therapist's variables. We discuss that considering alexithymia in case conceptualisation may help overcome its impact on the therapeutic process, especially regarding emotional processing and the therapeutic alliance. The implications for psychotherapy intervention are highlighted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".