Cognitive Failure and Alexithymia and Predicting High– Risk Behaviors of Students With Learning Disabilities
Bibliographic record
Abstract
BACKGROUND: One of the threatening health issues is prevalence of high-risk behaviors in various groups. Because of rapid social changes, it has been considered as of the most important problems of society by health organizations, administrative laws, and social policymakers. OBJECTIVES: The aim of this study was to determine the role of cognitive failure and alexithymia in predicting high-risk behaviors of students with learning disabilities. PATIENTS AND METHODS: This was a correlational research including all 14-16 years old students during 2012-2013 school year in Arak, IR Iran. Eighty students with learning disabilities were sampled by simply random sampling. The data were collected by cognitive failures questionnaire, Toronto alexithymia scale, and high-risk behavior questionnaire. RESULTS: The results showed that high-risk behaviors had significant positive correlations with difficulty identifying feelings (r = 0.321), difficulty describing feelings (r = 0.336), externally oriented thinking (r = 0.248), distractibility (0.292), memory distortion (r = 0.374), blunders (r = 0.335), and names amnesia (r = 0.275). Multiple regression analysis showed that cognitive failure and alexithymia predicted 32% of the total variance of high-risk behaviors. CONCLUSIONS: These findings demonstrated that cognitive failure and alexithymia had important roles in strengthening and appearance of high-risk behaviors in students with learning disabilities. Therefore, considering those problems, precautionary actions might be necessary.
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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 | 0.000 |
| 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) | 0.002 | 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".