The Association Between Personality Traits and Friendship Satisfaction among Undergraduates of Universities in Colombo District, Sri Lanka
Bibliographic record
Abstract
Background: Humans aspire to establish fulfilling friendships because the overall quality of friendship predicts psychological well-being. The quality of friendships can predict happiness, yet it can also be linked to unpleasant situations like conflict and betrayal. Many people, however, find it difficult to establish lasting and fulfilling connections. Objective: The main objective of the present study is to determine the relationship between personality traits and friendship satisfaction among undergraduates in the Colombo area. Methodology: A descriptive cross-sectional study was conducted among undergraduates in the Colombo area. A sample of 144 undergraduates completed an online questionnaire comprising the Big Five Personality Inventory (BFI) and McGill Friendship Satisfaction Questionnaire. The linear regression analysis was carried out using SPSS version 26. Results: The sample included 93 females (64.6%), and 50 males (34.7%) aged between 20 to 40 years (Mean age - 24±2.7). The results revealed a significant correlation between extraversion (r = +0.336, p<0.001), agreeableness (r = +0.226, p=0.006), openness (r = +0.268, p=0.001), and friendship satisfaction. Out of the Big Five traits, neuroticism (r = +0.070, p = 0.404) and conscientiousness (r = +0.114, p =0.175) were not significantly correlated with friendship satisfaction. Conclusion: Findings conclude that some personality traits (extraversion, agreeableness, and openness) were correlated with friendship satisfaction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".