A Brief Report of Psychometric Evaluation of the SWEMWBS in Adolescents across 7 Countries: Using MGCFA and IRT
Bibliographic record
Abstract
Objective: This study evaluates the psychometric properties of the Short Warwick–Edinburgh Mental Wellbeing Scale (SWEMWBS) among adolescents from seven culturally and linguistically diverse countries. Method: A total of 4,006 adolescents (ages 11–18) from 18 schools across Bahrain, Canada, Germany, India, Sweden, Turkey, and the United Arab Emirates completed the SWEMWBS as part of a school-based wellbeing survey. Participants responded in either English (n = 3,358) or French (n = 648). We examined internal consistency (Cronbach’s α and McDonald’s ω), conducted confirmatory factor analysis (CFA) to test unidimensionality, evaluated cross-national measurement invariance using multi-group CFA (MGCFA), and applied Item Response Theory (IRT) analyses to assess item discrimination, response thresholds, and measurement precision. Results: The SWEMWBS demonstrated acceptable-to-good internal consistency across all countries (α = .76–.82; ω = .76–.83) and a unidimensional structure with strong model fit (CFI = .983, RMSEA = .065). Full scalar invariance was supported across countries. IRT analyses showed that items 2 ("feeling useful") and 5 ("thinking clearly") offered the highest information, while country-specific patterns suggested subtle differences in item functioning. The test was most informative for adolescents with low-to-average mental wellbeing (θ ≈ −2.5 to +1.5). Intra-class correlation (ICC) indicated minimal school-level clustering (ICC = 0.037). Conclusion: Findings support the SWEMWBS as a reliable, unidimensional, and cross-culturally valid measure of adolescent mental wellbeing. These results provide psychometric justification for its use in global adolescent health research.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".