Exploring the Role of Family Resilience in Predicting Marital Functioning: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: This study aimed to investigate the predictive relationship between family resilience and family functioning among married couples. It sought to understand how the construct of resilience within the family context influences the overall dynamics and health of marital relationships. Methods and Materials: Adopting a cross-sectional design, the study recruited 250 married individuals from counseling centers and social network groups. Participants were assessed using standardized measures of family resilience and family functioning. Linear regression analysis was employed to explore the predictive power of family resilience on family functioning, with preliminary checks for multicollinearity, normality, and homoscedasticity. Findings: The analysis revealed a significant predictive relationship between family resilience and family functioning. Specifically, higher levels of reported family resilience were associated with better family functioning scores. These findings were supported by statistical analyses, demonstrating that family resilience accounted for a substantial portion of the variance in family functioning. Conclusion: The study confirms the importance of family resilience as a significant predictor of family functioning in married couples. This underscores the potential for interventions aimed at enhancing family resilience to positively impact marital health and well-being. The findings advocate for the integration of resilience-building strategies in family therapy and counseling practices.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".