Developing Emotional Intelligence: Approbation of a Coaching Program with Meditation Using Information and Communication Technologies
Bibliographic record
Abstract
The article is devoted to the analysis of the results of testing the coaching program on the development of emotional intelligence (EI) with the use of meditation, using the on-line format of coaching sessions. Relevance of the research is determined by the necessity of using different methods, including meditation and information and communication technologies, for ET development. Methods: theoretical analysis of the literature, psychological testing, questioning, formative experiment, methods of mathematical statistics (Kruskal-Wallis test, Mann-Whitney U-test, T-Wilcoxon test). Tests: 1) Manoilova emotional intelligence test, 2) Lyusin emotional intelligence test, 3) Toront alexithymic scale, 4) author's questionnaire. The sample is 45 people, 15 people were in the control group, 30 people were in two experimental groups. In one of them a coaching program was carried out for the development of ET without using meditative techniques, in the other - with their application. Conclusions of the study 1) In both experimental groups, unlike in the control group, there was a significant decrease in the level of alexithymia and an increase in most of the EI components. 2) Differences in the development of different components of EI were found depending on whether the program included the use of meditation or not. In the group in which meditation techniques were not used, the most significant changes occurred in the area of awareness of one's own and others' feelings. There were no significant changes in the ability to manage one's own and others' emotions. The indicators of interpersonal EI were also almost unchanged. In the group in which meditative techniques were used, along with an increase in the ability to be aware of one's own feelings and the feelings of others, the ability to manage the emotions of others increased. 3) The use of meditation is more conducive to developing the ability to be aware of one's own and others' feelings, and less conducive to managing the emotions of others. 4) The results indicate the effectiveness of the online coaching session format, but more research is needed to compare the effectiveness of the program when working online and face-to-face.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".