The Relationship between Emotional Intelligence and Marital Conflicts Using Actor-Partner Interdependence Model
Bibliographic record
Abstract
This study aimed to investigate the relationship between emotional intelligence and marital conflicts. This study utilizes a cross-sectional design to examine the relationship between emotional intelligence and marital conflicts. The participants were 100 married couples who were recruited through convenience sampling from different regions of Canada. The inclusion criteria for the study were that the couples had to be married for at least one year and have no history of mental illness. The participants were asked to complete the EQ-i questionnaire, which measures emotional intelligence, and the MCQ questionnaire, which measures the marital conflicts. The questionnaires were completed by both partners separately, and the responses were matched based on the couple's identification code. The data was collected through an online survey platform. The data analysis was conducted using the Actor-Partner Interdependence Model (APIM). The results for the APIM indicated that the husbands’ emotional intelligence (ß= -0.289, P<0.001) as well as the wives’ emotional intelligence (ß= -0.320, P<0.001) exhibited a significant actor effect on their marital conflicts. Similarly, husbands’ emotional intelligence (ß= -0.301, P<0.001) as well as the wives’ emotional intelligence (ß= -0.342, P<0.001) exhibited a significant partner effect on their spouses’ marital conflicts. The present study highlights the importance of emotional intelligence in romantic relationships and provides insights for clinicians and researchers working with couples to improve their marital relationships.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".