Effectiveness of Cognitive-Behavioral Therapy on Reducing Impulsive Behaviors, Alexithymia, and Despair in Depressed Patients at Counseling Centers in Ahvaz
Bibliographic record
Abstract
Background: Depression is one of the most prevalent mental disorders that severely affect activities and mental health. The present study aimed to investigate the effectiveness of cognitive-behavioral therapy (CBT) on reducing impulsive behaviors (IBs), alexithymia, and despair in depressed patients at counseling centers in Ahvaz. Methods: The research method was quasi-experimental with a pre-test, post-test, and one-month follow-up design, and a control group. The study population comprised all patients with depression who were referred to the counseling centers of Ahvaz in 2019. The sample consisted of 30 patients with depression selected by convenience sampling and divided into experimental and control groups (n= 15 per group). The experimental group underwent twelve sessions (90-minutes sessions per week) of cognitive-behavioral therapy. The research instruments included the Barratt Impulsiveness Scale (BIS), the Toronto Alexithymia Scale (TAS-20), and the Miller Hope Scale (MHS). The follow-up was performed after 30 days. Data were analyzed using multivariate analysis of covariance (MANCOVA). Results: The results showed that cognitive-behavioral therapy (CBT) reduced impulsive behaviors (IBs), alexithymia, and despair in the experimental group of depressed groups compared with the control group in the post-test and follow-up (Pvalue= 0.0001). Conclusions: CBT can be used at counseling centers for better treatment of IBs, alexithymia, and despair in depressed groups. Keywords: Cognitive-behavioral therapy, Impulsive behaviors, Alexithymia, Despair, Depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".