The effectiveness of compassionate mind-based therapy on cognitive and emotional processing deficits of adolescent soldiers aged 18 to 20 years with high-risk behaviors
Bibliographic record
Abstract
High-risk behaviors are defined as acts that increase the likelihood of physical, psychological and social disastrous consequences for the individuals. The aim of this study was to evaluate the effectiveness of compassionate mind therapy on cognitive deficits and emotional processing deficits among adolescent soldiers aged 18 to 20 years old with high-risk behaviors. The method of the present study was quasi-experimental with pre-test and post-test design. The population included all adolescent soldiers aged 18 to 20 years who referred to Valiasr Medical Center in Tehran in 2020. The sample consisted of 30 soldiers with high-risk behaviors who were purposefully selected among those who had completed the consent form based on entry and exit criteria. The selected individuals were randomly divided into two groups (15 people in each group). In order to collect data, Iranian adolescents’ risk-taking questionnaires, Cognitive Failures Questionnaire and Toronto Alexithymia scale were used. The experimental group was trained for eight sessions of compassion treatment and the control group did not receive any treatment. The data were analyzed using multivariate analysis of covariance and SPSS-23 software. The results showed that compassion-based therapy reduced cognitive deficits (p < .01) and emotional processing deficits (p < .05). According to the results of the present study, employing compassionate practice and increasing positive emotions can expand an individual’s behavioral-intellectual treasury, pave the way for successful problem solving, reduce negative intra-individual emotions, provide interpersonal skills, and thus reduce risky 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".