Correlation between depression-related personality dimensions and personality traits and its effects on depression
Bibliographic record
Abstract
Objective To explore the correlation between the higher-order personality dimension(neuroticism) and the lower-order personality traits(alexithymia, dependence, self-criticism, perfectionism) in the sample of university students, and explore the effect of the higher-order personality dimension and lower-order personality traits to depression in the same sample. Methods A convenient sample of 563 university students from the two Universities College in Hunan province.These students were assessed with Center for Epidemiological Studies-Depression Scale(CES-D), Depressive Experiences Questionnaire(DEQ), The twenty-item Toronto Alexithymia Scales(TAS-20), Frost multidimensional perfectionism scale(FMPS) and neuroticism subscale in EPQ. Results (1)There were significant relationships between the total score of CES-D, each depressive symptoms and 10 personality factors, such as neuroticism, doubts about action and so on(The coefficients ranged from 0.105 to 0.569, P<0.05 or P<0.01). (2)Factor analysis and multiple linear regression on the neurotic showed that doubts about action, concerned over mistakes, difficulties identifying feelings(DIF), difficulties describing feelings(DDF), dependency, self-criticism and neuroticism belonged to the factor 1(the factor load coefficients ranged from 0.574 to 0.775). (3)Neuroticism can explained 32.3% variance of depression(Radj2=0.323), after 6 personality factors entered the regression equation, such as difficulties describing feelings(DDF), parental criticism and so on, the explained variance of depression increased to 43.2%(Radj2=0.432). Conclusion There are overlapping and interaction between personality traits which include self-criticism, dependency, difficulties describing feelings(DDF), difficulties identifying feelings(DIF), concerned over the mistakes and neuroticism personality dimensions. The depressed affect are effectively predicted by neuroticism which is a effectively predict factor of depression, personality traits included self-criticism, dependency, alexithymia and malajustment perfectionism have a gain function of depression base on the Neuroticism. Key words: Depression; Personality dimension; Personality traits; Alexithymia
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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 | 0.000 | 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".