Subjective Well-Being of Students with Alexithymia in the Context of Development of their Emotional Competencies during the COVID-19 Pandemic
Bibliographic record
Abstract
Background. The question of the dynamics of the subjective well-being of students with alexithymia, who represent a risk group for the emergence of maladaptive states in stressful conditions during the COVID-19 pandemic, is considered in connection with their emotional competencies, reflecting the development of personal mechanisms for regulation (self-regulation) of emotions. Objective. The goal is to study and access the impact of the pandemic situation on the subjective well-being of students with symptoms of alexithymia through their emotional competencies. Methods. Toronto Alexithymia Scale by J. Taylor, Subjective Well-Being Scale by A.A. Rukavishnikov, emotional intelligence questionnaire by D.V. Lyusin, a technique for diagnosing emotional barriers in interpersonal communication by V.V. Boyko; Pearson r-test, Kruskal — Wallis H-test were applied. Sample. The study was conducted at the North Caucasus Federal University during the pre-COVID period (2018) and during the pandemic (2021). The sample consisted of 120 students. Results. As a result of a comparative analysis of signs of alexithymia, students found increased difficulties in describing feelings and externality of thinking during the pandemic. There were no significant differences in emotional barriers to communication before the pandemic and during the pandemic among alexithymics. A pronounced reluctance to get emotionally close to other people and a lower level of emotional effectiveness of communication, compared to students without alexithymia, were diagnosed in alexithymics both before and during the pandemic. During the pandemic period, alexithymics have identified: negative dynamics in the ability to understand and manage their own emotions, a noticeable decrease in subjective well-being to a feeling of pronounced emotional discomfort, especially on the scale of the significance of the social environment; negative correlations between the general indicator of alexithymia and difficulties in describing feelings, on the one hand, and satisfaction with everyday life, self-esteem of health, mood stability, ability to manage one's own emotions, on the other hand. Positive correlations with the control of emotional expression, depressive manifestations, absent-mindedness, drowsiness were also identified. Conclusion. The situation of the pandemic had a pronounced negative impact on the subjective well-being of students with alexithymia, mediated by barriers of emotional rapprochement with other people characteristic of alexithymics and a low level of intrapersonal emotional competencies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".