Investigating the Relationship between Theory of Mind Ability and Academic Achievement and Self-Efficiency of Students with Conduct Disorder in Ardabil
Bibliographic record
Abstract
The aim of this study was to determine the relationship between theory of mind ability and academic achievement and self-efficacy of students with conduct disorder in Ardabil. This descriptive study is correlational type, and the population of study consisted of all students with conduct disorder in high schools (secondary levels of 7, 8, 9) of Ardabil in March, 2015. Multi-stage cluster sampling method was used which covered 384 person and then Rutter’s behavioral disorders questionnaire form B was put at the disposal of teachers, and among people who were diagnosed with conduct disorder a total of 60 students with conduct disorder were selected as the sample group. Data were collected by the use of a questionnaire regarding self-efficiency in children and adolescents, Hopi’s theory of mind, behavioral disorder questionnaire by Rutter form B and academic records. Obtained information was analyzed by using Pearson correlation coefficient test and regression test. The results showed that there is a significant relationship between theory of mind with academic achievement, self-efficiency, social self-efficiency, academic self-efficiency and emotional self-efficacy (05/0>p). Regression analysis showed that theory of mind can predict significantly about 38% of the variances of academic achievement, 29% of the variances of self-efficiency, 26% of the variances of social self-efficiency, 41% of the variances of academic self-efficacy, and 28% of the variances of emotional self-efficiency in students. Accordingly, it can be concluded that theory of mind can predict academic achievement and self-efficiency in students with conduct disorder and it shows the relationship between these variables.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".