Moderating Effect of Age on the Link of Emotional Intelligence and Mental Health among High School Students
Bibliographic record
Abstract
This study examined whether, Emotional Intelligence (EI) can be considered as predictor for mental health and explored also the moderating effect of age on the link between EI with mental health among high school students. The participants in the study included 10th, 11th, and 12th grade students from 8 public high schools in Gorgan City, north of Iran. They were 247 high school students, specifically comprised of 124 boys and 123 girls, age ranged between 15 to 17 years old (83, Fifteen; 82, Sixteen; 82, Seventeen). The research design was an ex post facto and tested of alternative hypotheses. Two valid and reliable instruments were used to measure EI and mental health. Data analysis included frequencies, percentages, mean scores, simple regressions and moderated regressions. The result demonstrated that mental health can be influences by EI. In addition, age is not significant moderator for the relationships between EI with mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".