Prediction of Alexithymia Based on Abnormal Personality Dimension
Bibliographic record
Abstract
Objectives Alexithymia, as a personality trait, causes malfunctioning in individuals in three areas of recognizing emotions, describing emotions, and defective objective thinking.The present study aims to investigate the role of these three abnormal personality dimensions introduced in the 5th volume of the Diagnostic and Statistical Guide for Mental Disorders in the prediction of aspects of alexithymia.Based on previous works, we have hypothesized that there is a correlation between abnormal personality dimension and alexithymia dimensions.Methods In order to test the proposed hypothesis, a sample of high-school students of Ramsar County (N=250) were evaluated using the Personality Inventory for DSM-5 (PID-5) and Toronto Alexithymia Scale.Data were analyzed using SPSS18 and by the method of enter regressions. ResultsThe results indicated that there was a significant relationship between the inability to identify emotion and negative affect (r=0.28),disinhibition (r=0.20) and psychoticism (r=0.16).It was also revealed that there was a significant positive relationship between the description of emotion and negative affect (r=0.19),detachment (r=0.14),disinhibition (r=0.16) and psychoticism (r=0.27) and between objective thinking and all abnormal personality dimensions (P<0.01).Moreover, these personality dimensions can serve as a useful factor for predicting alexithymia.Conclusion According to the obtained correlations between alexithymia and abnormal personality dimension, it is necessary that the comorbidity of these two variables be considered in the treatment of personality and emotional 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 | 0.003 |
| 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".