The Effectiveness of Dialectical Behavior Therapy on Emotional Processing Defects and Impulsivity of Soldiers Aged 18 to 20 Years with High-risk Behaviors
Bibliographic record
Abstract
Background: Faulty emotional strategies are one of the most crucial indicators of dangerous behavior. Objectives: This study aimed to evaluate the effectiveness of dialectical behavior therapy (DBT) on emotional processing defects and impulsivity of soldiers aged 18 to 20 years with high-risk behaviors. Methods: The method of the present study was quasi-experimental with a pre-test-post-test design. The statistical population of this study included all soldiers aged 18 to 20 years referred to Valiasr Medical Center in Tehran in 2020. The research sample included 30 soldiers with high-risk behaviors who were selected purposefully and randomly assigned to two groups (15 people in the experimental group and 15 people in the control group). To collect data, the high-risk behaviors Scale (IARS), Barrett's Impulsiveness scale, and the Toronto Alexithymia Scale (TAS-20) was used. The experimental group underwent ten sessions of DBT, and the control group did not receive any treatment. Data were analyzed using multivariate analysis of covariance and SPSS-23 software. The significance level of the tests was considered 0.05. Results: The results of this study showed that the intervention and control groups had statistically significant differences in terms of emotional processing (P < 0.01) and impulsivity (P < 0.05) after the intervention of dialectical behavior therapy. Conclusions: Based on the findings of the present study, it can be concluded that dialectical behavioral therapy can be used along with other treatments to reduce the problems of soldiers with high-risk behaviors.
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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 | 0.000 | 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".