Emotional Memory and Emotional Intelligence of Individuals Diagnosed with Anti-Social Personality Disorder: Experimental Pretest-Posttest Design
Bibliographic record
Abstract
BACKGROUND: Aim of current study is to compare Emotional Memory (EM) and Emotional Intelligence (EI) between two groups of healthy people and individuals diagnosed with Antisocial Personality Disorder (ASPD).MATERIALS & METHODS: Current study is an experimental pretest-posttest study with case-group and control group, which was conducted between 2014-2015 at Zare Psychiatric hospital (Sari, Mazandaran Province, Iran). Statistical Society of this study was chosen via convenient sampling method; our sample was consisted of 80 individuals (men and women) that were divided into two groups of 40 healthy and 40 patients with APD. Subsequently, they answered to SCID-II (Structured Clinical Interview For DSM Disorders), Baron EQ-I (Emotional Quotient Inventory), WMS (Wechsler Memory Scale) and they looked at Picture Slides (Story). In order to analyze the findings of current study, Kolmogorov-Smirnov test, multiple covariance analysis (MANCOVA) and independent t-test were used.RESULTS: The findings showed that antisocial patients demonstrated lower score EM and EI.CONCLUSION: Emotional memory of ASPD individuals tends to be less than normal individuals. Furthermore, emotional intelligence of healthy individuals are higher that ASPD patients. It appears plausible that ASPD individuals tend to suffer in remembering their emotions due to their inability to retrieve emotional memories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".