Emotional Intelligence as a Predictive Variable of Alexithymia Among Students with Hearing Impairments
Bibliographic record
Abstract
The current research aims to study the relationship between alexithymia and emotional intelligence, and the possibility of predicting alexithymia among students with hearing impairments, based on the level of emotional intelligence, in light of the importance of emotional intelligence as an influential factor in the psychological and social adaptation of this group. The researcher used the descriptive correlational approach, and the research community consisted of secondary school students at the School for the Deaf and Hard of Hearing in Suez Governorate, where the number of members of the basic sample amounted to (50) male and female students. The Toronto Alexithymia Scale (TAS-20) and the modified Scott Emotional Intelligence Scale were applied, after verifying their psychometric properties of the scales, on a standardized sample of (102) male and female students from secondary school students at schools for the deaf and hard of hearing in Ismailia and Port Said Governorates. The research results showed the existence of a negative and statistically significant correlation between alexithymia and emotional intelligence among students with hearing disabilities at a significance level of (0.01). The results also indicated that the degree of alexithymia could be predicted through the level of emotional intelligence. The results did not show any statistically significant differences in alexithymia or emotional intelligence attributable to the variables of gender or degree of disability (moderate-severe).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".