Alexithymia and Therapist Reactions to the Patient: Expression of Positive Emotion as a Mediator
Bibliographic record
Abstract
This study examined patient's expression of emotion as a mediator of the relationship between patient alexithymia and therapist reactions to the patient. We analyzed data from 107 psychiatric outpatients who participated in a randomized controlled trial of two forms of group therapy for complicated grief. Patient alexithymia was assessed using the Toronto Alexithymia Scale-20. Patient's expression of emotion was assessed via sociometric ratings provided by other group members. Therapist reactions to the patient were assessed via therapist's ratings of each member in the group. Patient's expression of positive emotion met all the requirements for being considered a mediator. The findings indicated that the higher the level of alexithymia (specifically, greater difficulty communicating feelings and greater tendency to engage in externally oriented thinking), the less expression of positive emotion by the patient, and the more negative the therapist's reactions to the patient. The mediation provided by expression of positive emotion accounted for over half of the direct effect of alexithymia on therapist reactions to the patient. Future work needs to consider whether a therapist's awareness of a patient's limited capacity to express positive emotions and the negative influence that this can have on his/her reactions to his/her patients can help improve the therapeutic experience with alexithymic patients.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".