The Effectiveness of Assertiveness Training on Alexithymia and Self-Differentiation in Runaway Girls in the City of Mashhad, Iran
Bibliographic record
Abstract
Background: This study evaluates the effect of assertiveness training on alexithymia and self-differentiation in the city of Mashhad, in Iran, about runaway girls. Method: The sample consists of 24 girls (12-20 years old) The tools used in the study were: assertion questionnaire Rathus, self-differentiation scale of DSI and the Toronto alexithymia scale. The study is applied as a Quasi-experimental design with the unequal control group. The plan is similar to the control groups pretest and post-test. Results: Significance indexes Multivariate analysis (f=158.029, p<0.001) indicates that there are changes in alexithymia and self- differentiation with assertiveness training. The average assertion scores in the post-test had increased strongly (M=58.00). As well, the average self-differentiation score of the experimental group had a significant increase. The experimental group also had lower average scores of alexithymia. Conclusions: The results of the research showed that assertiveness training has a significant change in alexithymia and self-differentiation. It should be noted that the participants had run away from home due to various reasons relating to high levels of alexithymia and low levels of self-differentiation and assertiveness. During this training course, the girls with learning assertiveness made changes in the self-differentiation and alexithymia (M=196.00) (M=47).
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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".