Risk Factors for Depressive Disorders after Coming through COVID-19 and Emotional Intelligence of the Individual
Bibliographic record
Abstract
Background: COVID-19 has caused many new challenges for humanity worldwide. The pandemic united society from different regions of the planet in the experience of experiencing the epidemic, particularly complications after the disease, including the development of depression and increased anxiety. The study aimed to identify risk factors for depression among people who came through moderate and severe coronavirus infection and to substantiate the role of emotional intelligence as a factor that prevents depressive disorders. Methods: The author’s questionnaire, Beck’s Depression Inventory (BDI-II), Emotional Intelligence Test (EmIn), and narrative analysis were used for this purpose. Results: The separate groups of respondents, distributed according to their socio-economic status, were studied for their level of general emotional intelligence. High indicators of emotional intelligence of public sector employees who are in constant social interaction were recorded. A group of entrepreneurs focused on solving pragmatic financial and economic problems had low emotional intelligence. Severe depression symptoms were also the most common among a group of entrepreneurs. A decreased level of emotional intelligence in groups of female public sector employees and increased depressive symptoms were empirically found. The physiological factor was the most significant in contributing to depression. Conclusions: The main advantage of the study is the empirical justification of the role of internal anti-stress regulation mechanisms, with the development of emotional intelligence as one of the tools. Prospects for further research include improving diagnostic tools and studying the longer-term consequences of coronavirus disease, particularly in different groups of respondents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".