The Role of Personality Traits, Alexithymia, and Cognitive Avoidance in Predicting Positive and Negative Emotions
Bibliographic record
Abstract
Introduction: This study aims to study the role of personality traits, Alexithymia, and cognitive avoidance on positive and negative emotions.Method: The method of this research was descriptive and correlational. The statistical population includes all Bachelor students of Zanjan University aged 18-25 in the academic year 2018-2019. 220 people were selected as a statistical sample by the available sampling method. Using the Big Five Factors Questionnaire (McCreery and Costa, 1985), Toronto Emotional Dysfunction Scale (Bagby, Parker Taylor, 1994), Cognitive Avoidance Scale (Sexton and Dagas Ria, 2004), and positive and negative emotion scales (Watson, Clark and Telgen, 1989) were collected. Findings were analyzed using Pearson correlation and stepwise regression.Result: The results showed that neuroticism, difficulty in identifying feelings, difficulty in describing feelings, suppression of thought, substitution of thought, distraction, avoidance, and transformation of thought into thought have a positive and meaningful relationship. Extroversion, openness to experience, agreeableness, and conscientiousness have a negative and significant relationship with negative emotion. In addition, extroversion, agreeableness, conscientiousness, and avoidance have a positive and meaningful relationship. Also, there is neuroticism, difficulty in identifying the feeling, and difficulty in describing the feeling of negative and meaningful relationship with positive emotion.Discussion and conclusion: The results showed that personality traits, alexithymia, and cognitive avoidance are effective in positive and negative emotions. Based on the findings, it can be said that different personalities and people with alexithymia and cognitive avoidance experience different emotions to an unequal degree.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".