Alexithymia and Health-Related Quality of Life in Patients with Dizziness
Bibliographic record
Abstract
BACKGROUND: Alexithymia is a personality trait characterized by deficits in regulating, experiencing and verbalizing emotions and has been assumed to be associated with a tendency to express emotional arousal through somatization. Although such a tendency is often observed in patients with dizziness, the exact relationship of alexithymia to dizziness is not yet known. The aim of this study was to examine alexithymic characteristics in patients with dizziness and its relation to health-related quality of life (HRQoL). SAMPLING AND METHODS: We assessed 208 patients from an interdisciplinary center for vertigo and balance disorders for characteristics of alexithymia (20-item Toronto Alexithymia Scale), HRQoL (Short-Form 12 Health Survey, SF-12), dizziness (Dizziness Handicap Inventory), depression and anxiety (Hospital Anxiety and Depression Scale). Hierarchical regression analyses were used to evaluate the relationship between alexithymia, dizziness and HRQoL. RESULTS: We found that difficulties in identifying and describing feelings, two important factors of alexithymia, were significantly related to more severe symptoms of dizziness. More pronounced alexithymic characteristics were associated with lower HRQoL, especially in the mental dimension of the SF-12. The results remained significant after controlling for possibly confounding variables such as socioeconomic status and depression. CONCLUSIONS: These findings contribute to a better understanding of affect regulation in patients with dizziness, which is important for the development of psychotherapeutic interventions suitable for alexithymic patients with dizziness.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".