Psychometric validation of an Arabic version of the WHO-5 wellbeing scale among Lebanese Adolescents
Bibliographic record
Abstract
ABSTRACT Introduction Wellbeing in adolescence is frequently associated with positive developmental outcomes. The WHO-5 is widely recognized for its brevity, clarity, robust reliability, and cultural validity. In this study, we aimed to assess the psychometric properties of an Arabic version of the WHO-5 scale among Lebanese adolescents. Methods This cross-sectional study involved 681 Lebanese school students aged 15-18 years. Participants were assessed using WHO-5 (wellbeing), PHQ-9 (depression), and GAD-7 (anxiety) Arabic questionnaires at two-time points, 3 months apart. Results We found that the 5 items of the WHO-5 converged into a single factor. Composite reliability of scores was adequate in the total sample (ω = .83 / α = .83). The convergent validity for this model was satisfactory. We were able to show the invariance across gender at the configural, metric, and scalar levels, with males showing a higher level of wellbeing compared to females. The pre-posttest assessment for the WHO-5 scale was conducted on 358 participants; the intraclass correlation coefficient was adequate = 0.78 [95% CI .73; .82]. Our analyses also show that wellbeing was negatively correlated with depression (r = −.54; p < .001) and anxiety (r = −.52; p < .001). Conclusion The Arabic WHO-5 among Lebanese adolescents displayed highly satisfactory psychometric properties, which are evidence of its validity. It could be used to better track positive mental health in this vulnerable age group and could highlight efficiency in interventions aiming to promote wellbeing in adolescents. It could also potentially identify at risk individuals.
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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 | 0.003 | 0.005 |
| 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 | 0.000 |
| 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".