Empirical Study of Family Conflicts as a Factor of Emotional Burnout of a Woman
Bibliographic record
Abstract
Objective: Conflict issues have always been relevant to any society, but in Ukraine, at all stages of its development, conflicts had not just a noticeable place, but also influenced its history. Background: Conflicts, of course, adversely affect these processes, because a woman spends most of her life in the family and, of course, the climate inside the family affects the emotional state of all its members. Method: To study the relationship between the level of conflict in the family and the emotional burnout of a woman, we used the following research methods: empirical – methodologies of Y. Aleshina, L. Hoffmann, E. Dubovskaya "The nature of the interaction of the matrimony in conflict situations” and V. Boiko “Diagnostics of emotional burnout”, statistical – Pearson’s linear correlation coefficient. Results: The study involved 80 participants. The study also determined the relationship between the evaluation of the work-life balance and organizational parameters. As a result of the study, we determined that the syndrome most characteristic of women is exhaustion, at the average level – stress, at a low level, is a resistance. Conclusion: The study suggests that in families with a high level of conflict, there is emotional exhaustion in women.
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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 | 0.000 |
| 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".