The effect of acceptance and commitment therapy on emotional stability and alexithymia in female students
Bibliographic record
Abstract
Background: Mental disorders can cause many problems in students' academic performance and also severely affect their cognitive, emotional, moral and social development. The aim of this study was to evaluate the effectiveness of acceptance and commitment therapy (ACT) on emotional stability and alexithymia in female second-grade high school students. Methods: The present study was a quasi-experimental with a pretest-posttest design. The statistical population included all girls’ second grade students of the second secondary school in District 8 of Tehran. The sample size consisted of 118 students who were selected by stratified random sampling and assigned to two groups of 59 people as experimental and control groups). Data collection tools were emotional adjustment measure (EAM), Toronto alexithymia scale (TAS-20), and the ACT protocol. Data analysis was done using SPSS V.21 and multivariate analysis of covariance, and significance level was considered ≤ 0.05. Results: The findings showed that after adjusting the pre-test scores, ACT in the post-test has a significant effect on the Lack of regulation of emotional and physiological arousals, and despair and wishful thinking with values (F=90.143, F=178.324 respectively, P<0.001). Also, after adjusting the pre-test scores, ACT in the post-test showed a significant effect on the difficulty in identifying feelings (P<0.001, F=91.278), difficulty in describing feelings (P<0.001, F=189.328), and externally-oriented thinking (P<0.001, F=165.544). Conclusion: Based on the results, ACT training had a positive influence on emotional stability and alexithymia of high school students; the implementation of this protocol in schools and counseling centers is recommended.
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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) | 0.002 | 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".