Comparison of Acceptance Commitment Therapy (ACT) and Cognitive Behavioral Therapy (CBT) in Reducing Depression symptoms and Increasing Happiness of Iranian adolescent Girl Students
Bibliographic record
Abstract
This study aimed to compare the effectiveness of Acceptance and Commitment Therapy (ACT) and Cognitive-Behavioral Therapy (CBT) in reducing depression symptoms and increasing happiness of Iranian adolescent girls in Shiraz-Iran in 2017-2018 educational year. The research method was quasi- Experimental with assessing participants with pre -Test, Post-Test plans and control group. The Statistical population of this study consisted of 45 adolescent girl Students- between 13-17 years old - who were referred to the school’s student counselling centres because of their poor mental well being. They were selected by convenient sampling method and then they were randomly divided into three groups of 15 participants (two experimental groups and one control group). The Depression and Happiness variables were assessed by using Beck’s Depression Inventory (1996) and the Oxford Happiness Questionnaire (1989) respectively. Commitment Therapy Package was implemented for 8 sessions and Cognitive-Behavioral Package was implemented for 10 sessions for each Experimental groups separately. The Data were analysed by using SPSS24 software and analysis of multivariate covariance (MANCOVA). The results of this study suggested that both ACT and CBT Therapy approach had an acceptable effect on reducing Depression and increasing Happiness in Adolescent girls (p<0.05). However, the ACT had more influence on decreasing depression symptoms (1.56 %) and enhancing happiness (4.4. %) in participants outcomes in comparison with CBT method. Thus, it seems that ACT is a more effective intervention approach in this regard.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".