Family Dynamics: The Role of Emotional Expressiveness and Social Connectedness in Problem-Solving
Bibliographic record
Abstract
Objective: To investigate the relationships between family emotional expressiveness, social connectedness, and family problem-solving abilities. Methods: A cross-sectional study design was employed with a sample of 310 participants. Data were collected using the Family Assessment Device, Family Expressiveness Questionnaire, and Social Connectedness Scale. Descriptive statistics, Pearson correlation, and linear regression analyses were conducted using SPSS version 27. Results: Descriptive statistics revealed mean scores of 3.45 (SD = 0.67) for Family Problem-Solving, 4.12 (SD = 0.89) for Family Emotional Expressiveness, and 4.56 (SD = 0.78) for Social Connectedness. Significant positive correlations were found between Family Problem-Solving and Family Emotional Expressiveness (r = 0.58, p < .001), and Social Connectedness (r = 0.62, p < .001). The regression model was significant (F(2, 307) = 148.85, p < .001) with R^2 = 0.49, indicating that 49% of the variance in Family Problem-Solving was explained by Family Emotional Expressiveness (B = 0.38, β = 0.42, p < .001) and Social Connectedness (B = 0.47, β = 0.49, p < .001). Conclusion: The study highlights the significant roles of family emotional expressiveness and social connectedness in enhancing family problem-solving capabilities. Both factors were found to be positively correlated with and predictive of effective family problem-solving, suggesting that fostering open emotional communication and strong social ties can significantly contribute to family resilience and functionality.
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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 | 0.001 | 0.007 |
| 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) | 0.003 | 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".