Self- and Other-Focused Emotional Intelligence, Situational Emotional Understanding, and Experience of Loss
Bibliographic record
Abstract
The links between emotional intelligence and loss are under-researched, even though a lot of studies have investigated the psychological outcomes of traumatic experiences. Many people suffered multiple losses during the COVID-19 pandemic, including a loss of job, money, support services, or loved ones. The loss of a loved one might result in severe psychological trauma, and research suggests that early-life trauma relates to numerous forms of emotion dysregulation, including stress-reactivity. The consequences of loss for people with special needs deserve special attention since it often means not only the loss of a loved one but also the usual way of life. Thus, it is essential to analyze various aspects of loss experience, including the impact on emotional regulation, to reduce the harmful consequences of the COVID-19 pandemic. The purpose of this research was to examine emotional intelligence in groups of those who have a recent experience of loss and those who have not and to establish the impact of the experience of loss on the human psyche and mental health. We have analyzed the results of a simple random sample of gymnasium students (n=362). We hypothesized that the recent loss of a loved one diminishes the ability to understand self and other focused emotions as well as situational emotions. The survey has revealed that respondents who experienced the loss of a loved one understood better how individuals felt in the presented situations than those who did not have such experience. The premise that people who have experienced the loss of a loved one have a lower understanding of emotions than their peers who did not have such an experience has not been confirmed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".