Alexithymia and Interpersonal Problems
Bibliographic record
Abstract
BACKGROUND: Interpersonal relationships are substantially codetermined by nonverbal communication, e.g. facial affect. Given the deficits of nonverbal affect recognition and expression in alexithymia, we hypothesized that alexithymics had more interpersonal problems than nonalexithymic individuals, and that the various facets of the alexithymia construct are differentially related to interpersonal problems. METHOD: 149 subjects participating in an inpatient group psychotherapy program completed the Toronto Alexithymia Scale (TAS-20) and the Inventory of Interpersonal Problems (IIP-C) at the beginning of the treatment. The IIP-C was also administered to a subgroup at the end of the treatment. RESULTS: Based on the alexithymia scores, patients were classified as low- (TAS-20 score </=51), moderate- or high-alexithymic (TAS-20 score >/=61). High-alexithymic patients had significantly more interpersonal problems than low alexithymics, particularly in the IIP-C scales indicating hostility and social avoidance. The TAS-20 subscale difficulty describing feelings showed the highest correlations with interpersonal problems (r between 0.23 and 0.55). At the end of the treatment, the high alexithymics still scored highest on the IIP-C, but the magnitude of change in interpersonal problems did not differ across the groups. CONCLUSIONS: Our findings suggest that the interpersonal style of alexithymic individuals is characterized by a cold and socially avoidant behavior, corresponding to the predominantly insecure attachment pattern found in alexithymia. Additionally, our results indicate that group psychotherapy is as helpful for alexithymic as for nonalexithymic subjects with respect to interpersonal problems. Finally, we propose that alexithymia involves a reduced capacity to use social interactions for affect regulation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".