پیشبینی سازگاری اجتماعی براساس باورهای فراشناختی، ناگویی هیجانی و همدلی در دانشآموزان دچار اختلال یادگیری ویژه
Bibliographic record
Abstract
Students with specific learning disorder have adaptation problems due to lack of favorable social relationships and numerous academic problems. Therefore, the present study was conducted with the aim of predicting social adjustment based on metacognitive beliefs, alexithymia and empathy in students with special learning disabilities. In this descriptive and correlational study, 116 student were selected as a purposeful sample from all male and female students aged 10 to 14 years with specific learning disorder in Tabriz in 2019- 2020. Bell Social Adjustment Questionnaire, Wells Metacognitive Beliefs Questionnaire, Toronto Alexithymia Scale and Jolliffe & Farrington Empathy Scale were used to collect data. Data were analyzed by Pearson test and regression analysis. The results showed that social adjustment of students with specific learning disabilities had a positive and significant relationship and empathy and with metacognitive beliefs and alexithymia had a negative and significant relationship (p> 0.01). Metacognitive beliefs, alexithymia and empathy predicted 58% of the variance of social adjustment scores in students with specific learning disabilities (P <0.01). Considering the adverse consequences of learning disability and its widespread effects on the child's individual and social life, it is suggested that programs be implemented to promote appropriate metacognitive emotions and beliefs and to develop empathy among students with learning disabilities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".