The Effect Emotion Regulation Difficulties on Interoceptive Awareness and Alexithymia: An Example of Consultation-Liaison Psychiatry
Bibliographic record
Abstract
OBJECTIVE: This study investigated the relationships among interoceptive awareness, difficulties in emotion regulation, and alexithymia in a group of outpatients undergoing consultation liaison psychiatry (CLP). METHODS: Three hundred forty outpatients who applied to the Consultation Liaison Psychiatry Department were included in the study. Thirty-four patients who did not complete the questionnaires for various reasons were excluded from the study. Multidimensional Interoceptive Awareness (MAIA-2), Difficulties in Emotion Regulation Scale (DERS-16), and Toronto Alexithymia Scale (TAS-20) were applied to the participants. Statistics were performed with SPSS 21.0. Mediation analysis examined the relationship between interoceptive awareness, difficulties in emotion regulation, and alexithymia. RESULTS: It was found that 32% of outpatients who applied to CLP exhibited high alexithymic features. According to the study's results, interoceptive awareness had a significant negative relationship with difficulty in emotion regulation (r=-0.487, p<0.001). According to mediation analysis, difficulty in emotion regulation mediated the relationship between interoceptive awareness and alexithymia (β=-0.313; 95% confidence interval, -0.405 to -0.227; p<0.001). CONCLUSION: This study demonstrated the mediating effect of emotion regulation difficulties on the relationship between interoceptive awareness and alexithymia in outpatients applying to CLP. The use of interoception-based practices by mental health professionals working with CLP may reduce emotion control and alexithymia symptoms in this patient group.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".