The Contribution of Emotional Intelligence and Achievement Motivation on Psychological Well-Being
Bibliographic record
Abstract
The purpose of this study was contribution of Emotional intelligence and achievement motivation on psychological well-being of students in the Shahr-e-Qods University. The statistical population of this study included all the students of University from 2015 to 2016. A sample of 200 students has been selected through cluster sampling. These students responded to a set of questionnaires included emotional intelligence (EI), achievement motivation, and psychological well-being. Hierarchical regression analyses conducted for each dependent variable showed that emotional intelligence and achievement motivation could be considered as important indicators of psychological well-being (p<0.01).The results indicated that achievement motivation can predict psychological well-being, and among emotional intelligence components, self-control and self-awareness can predict psychological well-being. As the results indicated, growth and promotion of the emotional intelligence can be considered as methods for improving students' psychological well-being. This can be promoted and revolted through a rich Educational Environment, so it is recommended to teach emotional intelligence skills to students with low psychological well-being through training workshops.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".