The Role of Impulsive Behavior in Predicting the Emotional/ Behavioral Problems of Adults with Intellectual Disability
Bibliographic record
Abstract
The purpose of this research was to investigate the role of impulsive behavior in the prediction of the emotional/behavioral problems of adults with intellectual disability (ID). The statistical population included all adults with ID who were being trained in vocational rehabilitation centers, supported by the State Welfare Organization of Iran and the educational organization for children with special needs, in Shahrekord, Iran, in 2017-2018. The sample consisted of 134 adults with ID, selected through convenience sampling. The Barratt Impulsiveness Scale Version 11 (BIS-11) was used for measuring the impulsive behavior and The Developmental Behavior Checklist for Adults was used for measuring the emotional and behavioral problems. The collected data were analyzed using the Pearson correlation coefficient and simultaneous multiple regression. The results showed that impulsive behavior was a positive and significant predictor for emotional/behavioral problems and its subscales (P<0.01). Impulsive behaviors could predict emotional/behavioral problems such as self-absorbed problems, disruptive problems, antisocial problems, depressive problems, communication and anxiety disturbance and social relating problems. Therefore, designing and implementing preventive and interventional programs to improve the impulsive behavior of adults with ID appears to be necessary to reduce their emotional/behavioral problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".