Emotional intelligence trainhng ,alexithymia,general health,and academic achievement
Bibliographic record
Abstract
T he relationship between emotional intelligence training, Alexithymia, General Health, and academic achievement in high school students was investigated in this quasi-experimental study. To evaluate the nature of change in emotional intelligence based on acquired self-knowledge and actualization of emotional intelligence potential, 40 female high shool students were divided into experimental and witness group equally, and were adminstered the Self-report Measure of Emotional Intelligence (Schutte et al., 1998), Toronto Alexithymia Scale (Taylor & Bagby, 2000). General Health Questionnaire (Coldberg, 1972), and Symptoms CheckList-25-Revised (Najarian & Davoodi, 2001) tests. The experimental group participated in 10 sessions of emotional intelligence training based on theoretical pattern of Salovey and Mayer (1990), over a five week period. The previous test were administered a second time at the end of training sessions. The results of ANOVA and MANCOVA showed that emotional intelligence training only increased social skills (a subscale of emotional intelligence) and decreased difficulty in describing feelings (a subscale of alexithymia). No difference was observed among experimental and witness groups in terms of academic acheivement and general health.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".