Impact of Forgiveness on Marital Trust: Mediating Role of Emotional Reconciliation
Bibliographic record
Abstract
This study aimed to examine the effect of forgiveness on marital trust with emotional reconciliation as a mediating variable among married individuals in Hong Kong. A descriptive correlational design was employed using a sample of 381 married participants, selected based on Krejcie and Morgan's sample size table for large populations. Data were collected using standardized self-report questionnaires measuring forgiveness, emotional reconciliation, and marital trust. Descriptive statistics, Pearson correlation analysis, and Structural Equation Modeling (SEM) were conducted using SPSS-27 and AMOS-21 software. Assumptions for normality, linearity, multicollinearity, and homoscedasticity were tested and confirmed prior to the analysis. Pearson correlations revealed significant positive associations between forgiveness and emotional reconciliation (r = .62, p < .01), forgiveness and marital trust (r = .58, p < .01), and emotional reconciliation and marital trust (r = .55, p < .01). The SEM analysis demonstrated a good model fit (χ²/df = 2.14, CFI = 0.97, RMSEA = 0.045, TLI = 0.96). Forgiveness had a significant direct effect on emotional reconciliation (β = 0.62) and marital trust (β = 0.34), while emotional reconciliation significantly predicted marital trust (β = 0.37). The indirect effect of forgiveness on marital trust through emotional reconciliation was also significant (β = 0.23), indicating a partial mediating role. The total effect of forgiveness on marital trust was substantial (β = 0.57, p < .001). The findings suggest that forgiveness enhances marital trust both directly and indirectly through emotional reconciliation. These results underscore the importance of promoting emotional repair strategies in marital relationships to rebuild trust following interpersonal conflicts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".