The Effectiveness Of A Cognitive-Behavioral therapy program To Improve Delayed Language And Social Maturity Development among a group of Early Childhood Children in the Light Of Quality Of Life
Bibliographic record
Abstract
The study aimed to develop a cognitive-behavioral therapy program to enhance language development and social maturity among cases of Delayed Language Development among group of children in early childhood in light of the quality of life. Several measures (Quality of Life Scale prepared by the researcher, Arabic Language Scale prepared by Ahmed Abu Hasiba, Social Maturity Scale prepared by Vailanid) This is with the aim of identifying the impact of linguistic delay and social maturity on the children of early childhood, and the research has reached several results, the most important of which are: Proof of the effectiveness of the program in improving linguistic delay, social maturity and the quality of life in children of early childhood where the experimental group recorded a remarkable improvement in the post application of the measure of linguistic delay, social maturity and the quality of life compared to the control group, as the results of the language scale and maturity scale showed Social and the scale of the quality of life is a great disparity between the grades of the tribal application and the post application in favor of the post application, so the yelki cognitive therapy was an effective role in changing some of the wrong behaviors for children, which led to improving the linguistic growth of children, and the research recommends the need to benefit from the treatment programs and methods, which have proven effective in improving the linguistic side in children of early childhood stage.
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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".