Alexithymia and defense mechanisms in patients with depression, generalized anxiety disorder, obsessive-compulsive disorders and normal individuals: A comparative study
Bibliographic record
Abstract
Individuals with emotional problems experience uncontrollable and intensive negative affect. They do not have the ability to manage and regulate their acute emotional experiences. The main aim of the present study was to compare alexithymia and defense mechanisms among patients with Major Depression Disorder (MDD), Generalized Anxiety Disorder (GAD), Obsessive-Compulsive Disorder (OCD) and normal individuals. A total of 160 participants (40 patients with MDD, 40 patients with GAD, 40 patients with OCD, and 40 normal individuals) participated in this study. Following a psychiatric diagnosis of the disorders, participants were asked to complete the Farsi version of the Toronto Alexithymia scale-20 (FTAS-20) and Defense Styles Questionnaire (DSQ-40). The normal group also completed the Depression Anxiety Stress Scale (DASS-21) and were selected based on Structured Clinical Interview (SCID-I). The results demonstrated that there are significant differences between clinical and normal groups in terms of alexithymia and defense mechanisms (p<0.001). Significant differences among clinical groups were also found (p<0.001). The results of the present study could be used in diagnosis and differentiate among these three high prevalent comorbid disorders. The findings could be used in preventive and therapeutical programs for emotional problems.
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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 | 0.001 |
| 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".