Correlation between type D personality and alexithymia among patients with depressive disorder
Bibliographic record
Abstract
ObjectiveTo explore the relationship between alexithymia and type D personality in patients with depressive disorder， so as to further enrich the psychological theory of depressive disorder.MethodsFrom May to August 2020， 100 inpatients in Psychosomatic Medicine Department of Sichuan Provincial People's Hospital who met the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） for depressive episode were selected as the research objects. Basic information， type D personality， alexithymia and depressive symptoms of the patients were investigated via self-compiled general demographic questionnaire， Type D Personality Scale 14 （DS-14）， Toronto Alexithymia Scale （TAS-20） and Patients' Health Questionnaire Depression Scale-9 item （PHQ-9）.Spearman correlation analysis was used to test the correlation between the scores of each scale.ResultsA total of 82 patients with depressive disorder completed the survey， of whom 75 patients （91.46%） were found to have type D personality， and 50 patients （60.98%） were found to have alexithymia. The total scores of TAS-20， DS-14 and PHQ-9 were positively correlated （r=0.276~0.354， P<0.05 or 0.01）. TAS-20 total score and dimensional scores were positively correlated with social inhibition dimension score in DS-14 （r=0.224~0.375， P<0.05 or 0.01）. TAS-20 total score and the scores of difficulty in identifying feelings and difficulty in describing feelings dimensions were positively correlated the negative affectivity dimension score in DS-14 （r=0.257~0.341， P<0.05 or 0.01）.ConclusionAlexithymia is closely related to type D personality in patients with depressive disorder.
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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 | 0.000 | 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".