The Relationship between Alexithymia and Types C and D Personalities in People with Depression Disorders
Bibliographic record
Abstract
Background and Aim: Patients with depression disorders impose annually huge costs on their families and society. With a meticulous glance to the literature, lack of sufficient studies regarding Alexithymia and Types C and D Personalities are obvious. Thus, the aim of current study is investigation the relationship of Alexithymia with Types C and D Personalities in depressed individuals. Materials and Method: Current study through descriptive-correlation analysis examines the relationship between the mentioned variables on a sample of 150 patients. This sample was selected via simple random sampling method among all the depressed people in city of Tehran (as the statistical population) who in years 2012-2013 had visited treatment and diagnostic centers. The sample patients were asked to answer Eysenck Personality Questionnaire (EPQ) (1974), Denollet Type D Personality Questionnaire (1998), Torento-Alexithymia Scale 20 (TAS-20) (Bagby, Parker & Taylor, 1994). The obtained data were analyzed using bivariate regression analysis and Multivariate analysis of variance (MANOVA). Results: Findings demonstrated significant relationship between Alexithymia and Type C personality, but there was not significant relationship between Alexithymia and D personality type. In addition, there was no significant difference in terms of personality types between two genders, but women acquired higher score in Alexithymia and Type C personality than men. Conclusion: With respect to research results, close relationship between mood and personality was noticed and women had high vulnerability comparing to men. On this basis, it is necessary for clinicians to implement specific therapy measures to decrease Alexithymia in women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".