Examining the Relation between Emotional Intelligence and Happiness Status of Wellness Trainers
Bibliographic record
Abstract
The purpose of this study is examining the relation between the emotional intelligence and happiness of the wellness coaches. 390 wellness coaches 282 of whom were women and 108 of whom were men participated voluntarily in the study. The participants were actively working as wellness coaches. The Emotional Intelligence Scale (EIS) whose Turkish adaptation was realized by Aslan; and the Oxford Happiness Scale, which was developed by Hills and Argyle whose adaptation into Turkish was realized by Dogan and Sapmaz were used as the data collection tools. The descriptive statistical methods, the t-test, One-Way Variance Analysis (ANOVA), regression analysis and Pearson Correlation tests were used for the analysis of the data. At the end of the study it was determined that there was no statistically significant differences between the happiness and emotional intelligence points of Wellness Coaches for Oxford Happiness Scale when these points were compared according to the genders (p>0,05); and significant difference was determined in benefiting from emotions and optimism, which are the sub-dimensions of emotional intelligence scale (p<0,05); no statistically significant differences were found in the sub-dimension of the expression of emotions (p>0,05). It is observed that there is a positive relation at 0,383 level between the Oxford Happiness Scale and the Emotional Intelligence Scale. When the regression analysis results are examined, it is observed that the Emotional Intelligence Sub-dimensions explain the happiness at the medium level. The most explanatory one among the sub-dimensions is the optimism sub-dimension.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".