The Effect of Emotional Regulation for the Successful Treatment of Emotional Dependence in Young People
Bibliographic record
Abstract
Regulation of one’s own emotional state is of great importance for a person’s mental health. The issue under research is related to determining emotional regulation approaches for the success of the treatment of emotional dependence in young people. Methods. It was possible to achieve the set goal based on the use of methods of analysis, observation, the Spann-Fischer Codependency Scale, and the Student’s coefficient. The emotional regulation approaches developed by the authors included social recovery, analysis of someone else’s problem and behaviour, problem solving, and art therapy. Results.It was found that the therapy had a positive effect on the respondents, enabling them to primarily develop the self-confidence skills (96%). Also, to develop a lack of need for constant approval (92%), and consideration of their own interests (93%). It was found that the level of the respondents’ emotional dependence decreased to a low level (84%) from the beginning of the study. The respondents noted that art therapy (53%) and socialization (47%), which became the basis of the treatment approaches, had almost the same positive effect. Conclusions.The practical significance of the article is related to the possibility of using effective approaches to regulating emotional dependence in young people. The research prospects will be aimed at comparing the impact of the developed approaches to regulating emotional dependence in young people and middle-aged people.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".