Character Strengths as a Predictor of Adult Friendship Quality and Satisfaction: Implications for Psychological Interventions
Bibliographic record
Abstract
Since the birth of positive psychology, character strengths-based interventions aiming at promoting wellbeing have become very popular. However, there are only a few previous studies focusing on the associations of character strengths with social wellbeing, e.g., positive outcomes in close relationships. The aim of the present study was to investigate the associations between character strengths and positive adult friendship outcomes (i.e., friendship quality, satisfaction, and number of friends). The effects of age and gender were also examined. A total of 3051 adults aged from 18 to 65 years participated in the study. The Values-In-Action Inventory of Strengths-120 and the McGill Friendship Questionnaires (measuring friendship quality and satisfaction) were used. The results indicated that all character strengths positively correlated with friendship variables, while specific strengths predicted adult friendship quality (love, kindness, honesty, and curiosity), satisfaction (kindness, honesty, modesty, spirituality, love, and bravery), and number of friends (curiosity and persistence). Age, gender, and gender of the friend dyad (same and opposite-sex friendships) moderated only three of these effects. Practical implications for designing and implementing strengths-based positive friendship interventions in several contexts, such as university, workplace, and counselling are discussed.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".