The Role of Teachers’ Emotional Intelligence and Self-Efficacy in Decreasing Students’ Separation Anxiety Disorder
Bibliographic record
Abstract
Background: teachers are as responsible for personal progress of children as parents are for their nurturing. The purpose of this study is to examine the role of EI and self-efficacy of teachers in reduced SAD of primary school students in Tehran. In other words, this study evaluates the effective role of teachers in reducing SAD in students. Methods: This study used a descriptive-correlational methodology. The sample consisted of 345 teachers and 280 students with SAD selected by stratified proportional to size sampling by Cochran formula. Bar-on’s EQ-i, Schwarzer’s GSE and evaluation forms were used to evaluate teachers; Espada’s CSAS and Spence Children’s Anxiety Scale (SCAS) were used to measure SAD in children. Finally, post-test was taken from students with SAD.Results: The results showed a significant positive correlation between EI and self-efficacy of teachers. On the other hand, EI and self-efficacy of teachers significantly influenced students, so that a significant difference was found in the pre-test and post-test scores of students. SAD significantly decreased in students. Conclusion: positive teacher-student interactions can reduce the symptoms of SAD in students. Thus, teaching profession is a serious responsibility which should not be considered only as a job.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".