Examınatıon of the Relatıonshıp between Alexıthymıa, Anger and Defense Mechanısm
Bibliographic record
Abstract
Introduction and Aim: It is thought that immature and neurotic defense mechanisms, especially splitting and introjection, play an important role in alexithymia. The aim of this study is to examine the relationship between alexithymia, anger and defense mechanisms in a non-clinical sample Materials and Methods: This study was prepared in accordance with the correlational survey model. The sample selection of the study was made using simple random sampling.The sample group of the study consists of 430 (50.1%) women and 427 (49.9%) men living in Istanbul.The research data collection process took place between 2019-2020. Results: There is a weak positive relationship between Anger Symptoms and Immature Defenses, and a weak and negative relationship with Mature Defenses. There is a weak and positive relationship between Situations Leading to Anger and Immature Defenses. There is a weak, positive relationship between Anger-Related Thoughts and Immature Defenses, and a moderate and negative relationship between Anger-Related Thoughts and Mature Defenses. There is a weak positive relationship between Anger-Related Behaviors and Imature Defenses, and a weak and positive relationship between Anger-Related Behaviors and Neurotic Defenses. Conclusion: It was determined that there is a significant relationship between the the sub-dimensions of the multidimensional anger scale and the sub-dimensions of the defense mechanisms and between the sub-dimensions of the multidimensional anger scale and the sub-dimensions of the Toronto alexithymia scale. Keywords: Anger, Alexithymia, Defense Mechanisms, Introjection
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".