Perceptions about brain health among the United Arab Emirates population using the global brain survey: a cross-sectional study
Bibliographic record
Abstract
Introduction: Interest in brain health and general well-being research has increased due to advances in neurosciences, and aging population's need for preventive health measures. However, there is limited research on perceptions and attitudes toward brain health in the United Arab Emirates (UAE), a country with a unique demographic and cultural context. We aimed to assess self-reported practices, beliefs and attitudes toward brain health within the UAE population, identifying key factors influencing these views, and contributing to the global understanding of brain health in non-Western, high-income settings. Methods: We conducted a cross-sectional study using the UAE-adapted Global Brain Health Survey, originally developed by the Lifebrain Consortium in Europe. The survey was distributed in both English and Arabic language via social media and the snowball technique. Data analysis included descriptive statistics and results of multivariable binary logistic regression. Results: A total of 931 responses were recorded and analyzed. Overall, participants demonstrated a moderate understanding of brain health. Key factors that participants believed to influence brain health were physical health, sleep habits, substance use, and social environment. Older participants were more likely to engage in healthy lifestyle choices, while younger participants prioritized different activities. We also observed gender differences, with women less likely to engage in activities such as taking nutritional supplements and practicing relaxation techniques. Higher education and healthcare experience were linked to more informed perceptions of brain health. Discussion: Our findings provide valuable insights into how cultural, social, and demographic factors influence the practices and beliefs toward brain health in the UAE. By adapting the Global Brain Health Survey for a Middle Eastern context, we demonstrate its cross-cultural applicability and contribute to the global discourse on brain health. Our results may inform future public health policies and interventions, highlighting the importance of tailored culturally sensitive strategies to promote brain health across different demographic groups, particularly in multicultural and rapidly aging societies.
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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 | 0.002 |
| 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 | 0.001 | 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".