Social Connectedness as a Function of Emotional Regulation and Cultural Tolerance: A Psychological Perspective
Bibliographic record
Abstract
Objective: This study aimed to examine the predictive role of emotional regulation and cultural tolerance in social connectedness, determining the extent to which these psychological factors contribute to interpersonal relationships. Methods and Materials: A correlational descriptive design was employed, with 400 participants selected using Morgan and Krejcie’s (1970) sample size table. Participants completed the Social Connectedness Scale (SCS), Emotion Regulation Questionnaire (ERQ), and Cultural Tolerance Scale (CTS). Pearson correlation analysis was conducted to assess the relationships between variables, while a multiple regression analysis determined the predictive value of emotional regulation and cultural tolerance for social connectedness. Statistical analyses were performed using SPSS-27, with a significance level set at p < 0.01. Findings: Descriptive statistics revealed a mean social connectedness score of 42.65 (SD = 6.87), an emotional regulation mean of 51.24 (SD = 8.14), and a cultural tolerance mean of 37.89 (SD = 7.92). Pearson correlation analysis indicated significant positive relationships between emotional regulation and social connectedness (r = 0.58, p < 0.01) and between cultural tolerance and social connectedness (r = 0.49, p < 0.01). The regression model was statistically significant (F(2,397) = 99.32, p < 0.01), explaining 38% of the variance (R² = 0.38, Adjusted R² = 0.37). Both emotional regulation (B = 0.45, β = 0.41, p < 0.01) and cultural tolerance (B = 0.38, β = 0.33, p < 0.01) significantly predicted social connectedness. Conclusion: The findings confirm that emotional regulation and cultural tolerance are significant predictors of social connectedness. Individuals with higher emotional regulation skills and greater openness to cultural diversity tend to experience stronger social bonds. These results highlight the importance of fostering emotional and cultural adaptability to promote social integration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".