Effects of Cognitive Behavioral Group Therapy on Reduction of Anxious Thoughts among Selected High School Students in Iran: A Case Study
Bibliographic record
Abstract
Cognitive behavioral group therapy (CBT) helps reduce individuals’ anxious thoughts. Considering this, the present study aimed to determine the level of anxious thoughts among selected Iranian high school students and to explore whether cognitive behavioral group therapy curtails anxious thoughts among Iranian high school students. To do so, a one-group pretest-posttest design was adopted (quantitative design) in this research, to measure the effectiveness of anxious thoughts-reduction policies to reduce anxiety among Iranian high school students. To address the research objectives, 20 teenagers who were students at some schools in Mashhad, Iran, participated in this study after taking the Anxious Thoughts Inventory (AnTI) designed by Wells (1994). The analysis of the data obtained from implementing the questionnaires was performed through SPSS 25 software in two sections: descriptive and inferential (Paired sample t-test). The results revealed that the means score of the students at the beginning of the experiment in the pretest at Anxious Thoughts Inventory was 63 which was a sever level of anxious thoughts at the pretest. The item “I worry about my appearance” remained on top both in the pretest and posttest. A paired t-test was used and showed that there was a significant difference in the pretest and posttest scores of anxious thoughts of the participants after using the cognitive behavioral group therapy. Therefore, it can be concluded that cognitive behavioral group therapy curtails anxious thoughts effectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".