Examining the Link Between Character Strengths and Positive and Negative Mental Health Indicators in Iranian Adolescents
Bibliographic record
Abstract
Character strengths are an important foundation for positive development and thriving in adolescence. Most research on character strengths has been conducted with youths in Western cultures. We examined character strengths in relation to positive and negative well-being indicators in a sample of Iranian youths. We investigated the reliability and validity of the Farsi version of the Values in Action Inventory of Strengths for Youth (VIA Y-96)—a self-report survey commonly used in Western contexts. Participants were adolescents in Iran ( N = 1,359; 48.5% female; M age = 13.54, SD = 1.00) who completed the VIA Y-96 and questionnaires assessing life satisfaction, positive/negative experiences, depression, anxiety, and stress. We found that reliability coefficients were acceptable for most of the VIA subscales. A confirmatory factor analysis (CFA), as well as a second-order CFA, supported the construct validity of the Farsi VIA Y-96. Correlations between the character strengths and positive and negative well-being indicators supported convergent validity. Measurement invariance for the VIA Y-96 was established in this study comparing boys’ and girls’ response patterns. Sex and grade level differences were found for some of the subscales. Overall, the Farsi VIA Y-96 had acceptable psychometric properties, suggesting that it can be used in research on character strengths with Iranian youths.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".