Effects of a Transactional Analysis Program on Adolescents’ Emotion Regulation
Bibliographic record
Abstract
Most adolescents are susceptible to exhibit emotional disorders due to rapid changes that occur during adolescence (Rudolph, 2002). To cope with such an issue, it is required to apply intervention programs in order to develop their competencies (Viner et al., 2012). In this study, the effect of transactional analysis on emotion regulation of 10th-grade female high school students has been examined by utilizing a quasi-experimental research (pre-test, post-test, and a control group design). Two classes have been chosen by cluster sampling and randomly assigned as the experimental and control groups. The Regulation of Emotion Questionnaire (Phillips & power, 2007) was administrated. The transactional analysis program has been hold in eight sessions for the experimental group. Both groups were reexamined for follow-up a month later. The collected data were analyzed by Multivariate Analysis of Covariance (MANCOVA) indicates a significant increase in the functional emotion regulation strategies as well as a marked decrease in the dysfunctional emotion regulation strategies. The follow-up test also revealed an adequate stability. The implications of transactional analysis program will be discussed.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".