The Impact of Emotional Intelligence and Resilience on Family Cohesion: Insights from Married Individuals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: This study aimed to explore the predictive influence of emotional intelligence and resilience on family cohesion among married individuals. By examining these relationships, the research sought to identify potential psychological traits that could be targeted to enhance family dynamics and cohesion. Methods and Materials: Employing a cross-sectional design, the study sampled 250 married participants recruited from counseling centers and social network groups. Emotional intelligence and resilience were assessed using standardized and validated scales, with family cohesion measured through a comprehensive family cohesion assessment tool. Linear regression analysis was conducted to determine the predictive relationship between emotional intelligence, resilience, and family cohesion, with preliminary tests ensuring adherence to statistical assumptions. Findings: The results demonstrated that both emotional intelligence and resilience significantly predict family cohesion, accounting for a substantial proportion of the variance in cohesion scores among the participants. Specifically, emotional intelligence and resilience emerged as strong predictors, highlighting their crucial role in fostering positive family relationships. Conclusion: The study underscores the importance of emotional intelligence and resilience as key factors in promoting family cohesion. These findings suggest that interventions aimed at enhancing these psychological traits could be beneficial in improving the quality of family dynamics. Future research should further investigate these relationships longitudinally and across diverse populations to validate and extend these findings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it