Temperament, Character Traits and Alexithymia in Patients with Asthma; A University Hospital Sample
Bibliographic record
Abstract
Background: The relationship between psychological factors and asthma has received attention since very early times. However, there’s a lack of knowledge about the temperament and character features of asthma patients. We aimed to assess if there are specific personality traits in asthma patients.Methods: Thirty-eight patients with asthma and thirty-five healthy individuals were enrolled in the study. The sociodemographic data form and Temperament and Character Inventory (TCI) and Toronto Alexithymia Scale (TAS) were applied to all participants. Results: The mean age was 40.9 ± 15.9 in the asthma group (52.1%) and 37.3 ± 14.3 in the control group (47.9%). There were 29 females and 9 males in asthma group, 19 females and 16 males in control group. In analysis of temperament and character subscales, the scores of harm avoidance, frugality, sentimentality and transpersonal identification were higher in asthma patients than the control group; the differences were statistically significant (p<0.05). There were no significant differences between the groups for alexithymia (p>0.05).Conclusions: Our study showed that there were character and temperament differences between asthma patients and healthy group. There were no differences for alexithymia between the groups. Further studies are needed to evaluate the causes of differences and the impacts of traits on disease course.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".