Dynamics of Relationship Maintenance: The Influence of Emotional Support and Trust
Bibliographic record
Abstract
Objective: The objective of this study was to investigate the roles of emotional support and trust in predicting relationship maintenance in romantic couples. Methods and Materials: A cross-sectional design was employed, involving 280 participants in committed romantic relationships. Participants completed standardized questionnaires measuring relationship maintenance, emotional support, and trust. Data were analyzed using Pearson correlation and linear regression analyses to explore the relationships between these variables. SPSS-27 software was utilized for statistical analysis, and assumptions of normality, linearity, homoscedasticity, and multicollinearity were checked and confirmed. Findings: The regression model indicated that emotional support (β = 0.46, p < 0.001) and trust (β = 0.38, p < 0.001) were significant predictors of relationship maintenance, explaining 46% of the variance (R² = 0.46, F (2, 277) = 118.36, p < 0.001). The findings underscore the critical roles of emotional support and trust in relationship maintenance. Both factors significantly contribute to sustaining romantic relationships, with emotional support showing a slightly stronger influence. Conclusion: These insights can inform relationship counseling and interventions aimed at enhancing relationship stability through targeted support and trust-building activities.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| 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".