Cognitive Failures and Sense of Coherence as Predictors of Academic Resilience in Children with Speech Impairments
Bibliographic record
Abstract
This study aims to investigate the relationship between cognitive failures, sense of coherence, and academic resilience in children with speech impairments. The objective is to determine how cognitive and psychological factors predict resilience in this population. A cross-sectional design was employed, involving 376 children with speech impairments, aged 8 to 12 years. Participants were recruited from special education centers and mainstream schools. Academic resilience, cognitive failures, and sense of coherence were measured using the Academic Resilience Scale (ARS-30), Cognitive Failures Questionnaire (CFQ), and Sense of Coherence Scale (SOC-29), respectively. Pearson correlation analysis examined the relationships between variables, and linear regression analysis determined the predictive value of cognitive failures and sense of coherence on academic resilience. Data analysis was conducted using SPSS version 27. Descriptive statistics indicated moderate levels of academic resilience (M = 78.45, SD = 11.32) among participants. Cognitive failures (M = 43.29, SD = 9.87) negatively correlated with academic resilience (r = -0.56, p < .001), while sense of coherence (M = 65.14, SD = 10.45) positively correlated (r = 0.63, p < .001). The regression model was significant (F(2, 373) = 173.27, p < .001), explaining 48% of the variance in academic resilience (R² = 0.48). Cognitive failures (B = -0.45, p < .001) and sense of coherence (B = 0.59, p < .001) were significant predictors of academic resilience. The findings underscore the significant roles of cognitive failures and sense of coherence in predicting academic resilience in children with speech impairments. Interventions targeting cognitive improvement and psychological support can enhance resilience, thereby promoting better academic outcomes. Future research should explore longitudinal trajectories and the effectiveness of specific interventions to further support these children.
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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 | 0.007 |
| 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) | 0.002 | 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".