Validation and psychometric testing of the Arabic version of the mental health literacy scale among the Saudi Arabian general population
Bibliographic record
Abstract
OBJECTIVE: This study aimed to validate the Arabic Version of the Mental Health Literacy Scale (Arabic-MHLS) among the Saudi Arabian general population, assessing its internal consistency, test-retest reliability, and structural validity. METHODS: A total of 700 Arabic-speaking Saudi adults were randomly selected to complete the electronic questionnaire in May 2023, which generated 544 participants. Data were coded and stored in the ZdataCloud research data collection system database. Test-retest reliability was assessed using a subsample of 48 participants who completed the questionnaire twice, with a one-week interval. Structural validity was examined using confirmatory factor analysis (CFA) and Exploratory Factor Analysis (EFA). RESULTS: The Arabic-MHLS demonstrated good internal consistency (Cronbach's alpha = 0.87) and test-retest reliability (intraclass correlation coefficient = 0.89). EFA revealed a four-factor model closely resembling the model identified in the Slovenian validation of MHLS, with factor loadings ranging from 0.40 to 0.85. The four factors included knowledge of mental health disorders, knowledge of help-seeking, knowledge of self-help strategies, and knowledge of professional help also showed good internal consistency. CONCLUSION: The Arabic-MHLS is a valid and reliable tool for assessing mental health literacy in the Saudi Arabian general population. However, further research is needed to refine the measurement tool and understand the complex relationships between mental health literacy and other mental health-related concepts. This will contribute to the development of targeted interventions and policies aimed at improving mental health literacy and promoting mental well-being in the Saudi Arabian population and beyond.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".