Understanding Family Functioning: The Influence of Cultural Marginalization and Social Competence
Bibliographic record
Abstract
Objective: The objective of this study was to investigate the relationships between family functioning, cultural marginalization, and social competence. Specifically, it aimed to understand how cultural marginalization and social competence individually and collectively predict family functioning. Methods: This cross-sectional study involved 350 participants selected based on the Morgan and Krejcie sample size table. Data were collected using the Family Assessment Device (FAD) for family functioning, the Cultural Marginalization Scale (CMS) for cultural marginalization, and the Social Skills Inventory (SSI) for social competence. Descriptive statistics, Pearson correlation coefficients, and linear regression analysis were conducted using SPSS version 27 to analyze the data. Results: The mean score for family functioning was 2.75 (SD = 0.68), cultural marginalization was 3.45 (SD = 0.72), and social competence was 4.12 (SD = 0.55). Significant correlations were found between family functioning and cultural marginalization (r = -0.52, p < 0.001) as well as social competence (r = 0.46, p < 0.001). Regression analysis revealed that cultural marginalization (β = -0.42, p < 0.001) and social competence (β = 0.28, p < 0.001) significantly predicted family functioning, accounting for 22% of the variance (R² = 0.22, F(2, 347) = 45.67, p < 0.001). Conclusion: The study concluded that cultural marginalization and social competence are significant predictors of family functioning. Cultural marginalization negatively impacts family dynamics, while social competence positively influences them. These findings highlight the need for culturally sensitive interventions to support marginalized families and enhance their social skills to improve overall family functioning.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".