Predicting alexithymia based on attachment styles and perfectionism dimensions
Bibliographic record
Abstract
Background: Alexithymia is difficulty in describing, differentiating and regulating emotions. This construct is rooted in the relatively stable emotional bond of the child with the primary caregiver and it continues under the influence of one's effort to avoid emotions by focusing on perfectness and hiding defects. Based on these, does it possible to predict alexithymia by attachment styles and dimentions of perfection? Aims: The present study was carried to predict alexithymia based on attachment styles and dimensions of perfectionism in students of University of Tehran. Method: current study is descriptive-correlation research. 268 students (37 women and 231 men) from University of Tehran completed the Adult Attachment Inventory (Besharat, 1392), Tehran's Multidimensional Perfectionism Scale (Besharat, 1386) and Farsi Toronto Alexithymia Scale 20 (Besharat, 2007) by accessible sampeling, voluntarily. Results: The analysis of data involves Pearson’s correlation coefficient and simultaneous regression analytic. The results demonstrated that as the self-oriented (p<0/01, r=0/30), other-oriented (p<0/01, r=-0/26) and social-oriented (p<0/05, r=0/14) perfectionism increased, the scores of alexithymia increased too. Results also showed that increasing in secure attachment subscale (p<0/01, r=-0/35), the alexithymia score reduce and an increasing in avoidance (p<0/01, r=0/38) and ambivalence (p<0/01, r=0/38) attachment sunscales, the alexithymia score increased. Findings showed that secure, avoidance, and ambivalent attachment styles, along with self-oriented perfectionism, explained 26% of alexithymia variances (p<0/01) Conclusions: This means that child's failure to have a secure relationship with the caregiver leads to alexithymia and perfectionism that operate as a means to self-expression during the development also delay recognition and description of emotions so that alexithymia persists.
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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 | 0.001 | 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".