The Impact of a Play-Based Training Program on Reducing the Negative Effects of Students Abuse and Improving Their Self-Esteem
Bibliographic record
Abstract
The aim of this study was to investigate the effect of a play-based program on reducing the effects of student’s abuse and to improving their self-esteem. To achieve this goal, a sample was selected and composed of 60 male and female abused students. They were divided into two groups: control group and experimental group which both consisted of 30 students. The experimental group was divided into two groups: the first consisted of 15 male students, while the second consisted of 15 female students. The measure of exposure to abuse and the self-assessment scale were applied as an anterior and posterior test of both experimental and control groups. The experimental group was only exposed to training through the training program. After completing the data collection, the means and standard deviations of the performance of the experimental and control groups were calculated on the study scales. The multivariate variance analysis was also conducted to identify the impact of the training program on gender. The results of this study showed statistically significant differences at the mean level (α ≤ 0.05) between the performance mean of experimental and control groups. These differences were in favor of the experimental group that improved their level of self-esteem and decreased the level of exposure to abuse. The results also showed that there were no statistically significant differences at the level of significance (α ≤ 0.05) between the female and male performance means.
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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) | 0.003 | 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".