Investigation of the relationship between alexithymia and eating attitude, self-esteem and anger in women who applied to psychological counseling center
Bibliographic record
Abstract
Objective: The main purpose of this study was to examine self-esteem, anger and eating attitude with alexithymia in people who consult to the psychological counseling center. The sample of the study consist of 100 women who were located in Istanbul and applied to the psychological consulting center.Methods: Consent form, demographic form, State-Trait Anger Expression Inventory, Rosenberg Self-Esteem Inventory, Toronto Alexithymia Scale and Eating Attitude Test have been used to collect the data in this study. In this research, the relationship between sociodemographic features, alexithymia, self-esteem, anger, eating attitude of women who consult to the psychological counseling center had been examined. Data obtained have been analyzed statistically by using SPSS 15.0 for Windows.Results: The findings of this research supported our hypothesizes. Regarding the relationship between eating attitude and alexithymia, there is a positive relationship between difficulties in identifying and describing feelings with eating attitudes (p < 0.05). Regarding the relationship between alexithymia and self-esteem, there is a positive relation between sub-dimensions of alexithymia scale with subscales of self-esteem (p < 0.05). There is a positive relationship between identifying feelings with trait anger and anger-out scores (p < 0.05).Conclusions: According to these results, it appears that alexithymia has a serious relationship with anger, eating attitude and self-esteem. We suggest that alexithymia may be at the center of other features relations with each other.
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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) | 0.002 | 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".