Prevalence of Undiagnosed Attention Deficit Hyperactivity Disorder (ADHD) Symptoms in the Young Adult Population of the United Arab Emirates: A National Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Attention deficit hyperactivity disorder (ADHD), a globally prevalent behavioural disorder, remains underdiagnosed, particularly among adults. This issue is exacerbated in the Arab region due to stigma and insufficient healthcare facilities and professionals. Despite the United Arab Emirates (UAE) efforts to improve mental healthcare, shortcomings persist. No studies in the UAE currently assesses the appropriateness of the screening system for ADHD and other behavioural issues. Furthermore, prevalence rates of ADHD, particularly within the young adult population, are absent. AIM: To estimate the prevalence of ADHD amongst young adults attending university in UAE and examine its relationship with gender and academic outcomes. METHODS: A cross-sectional, correlational design was used. Young adults in their first year at university were recruited from different academic institutions across the UAE. The study utilized the Adult ADHD Self-Report Scale (ASRS) for data collection. RESULTS: A sample of 406 young adults, aged between 18 and 20 years of age were recruited. Approximately, 34.7% (n = 141) reported symptoms suggestive of probable ADHD. Significantly lower grade point average marks were observed in participants with ADHD symptoms (M = 3.15) compared to those without (M = 3.35). Females reported symptoms of probable ADHD at higher rates than males, indicating possibly a potential screening deficiency and a potential stigma consequence. CONCLUSIONS: The study demonstrates a high prevalence of probable ADHD in young adults, particularly among females attending university in the United Arab Emirates. Implications for early screening, service provision, and greater professional health training on this disorder are required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".