Digital health determinants & divide in the Arab world: A cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Digital determinants of health include key technological factors such as internet access, digital literacy, and the quality of online health information. These elements critically influence health outcomes and behaviors. METHODS: This study examined the impact of digital health determinants on health improvement across ten Arab countries: Bahrain, Palestine, Lebanon, Jordan, Kuwait, the United Arab Emirates, Saudi Arabia, Egypt, Morocco, and Tunisia. The study analyzed a dataset of 12,522 samples after implementing SMOTE-ENN to balance underrepresented demographics, capturing data on digital literacy, internet access, and the impact of online health information on personal health. RESULTS: Results showed that 93.9% of participants reported having internet access, yet 71.4% did not receive formal education on internet usage. Morocco, Tunisia, and Jordan reported the highest percentages of individuals without such education. Regarding health impacts, 32.9% of participants reported significant personal health improvements linked to digital determinants. Egypt, Lebanon, and Saudi Arabia had higher rates of positive health impacts, while Morocco, Jordan, and Bahrain reported the lowest health improvements. Higher digital literacy and reliable internet access were positively associated with better health outcomes across all countries, whereas specific sociodemographic and digital factors varied: younger age and urban residence were linked to greater benefit in the Gulf; education level and healthcare access were especially influential in North Africa; and in the Levant, digital literacy and use of trusted health sources showed strong impact. These findings show both shared and region-specific drivers of digital health benefits. CONCLUSION: Improving health outcomes requires diversification: foundational education on internet usage must be combined with broader digital literacy initiatives, efforts to build and maintain trust in credible online health platforms, and strategies that actively foster patient engagement through interactive digital tools. Policies should also ensure reliable internet infrastructure and tailor interventions to regional and sociodemographic contexts to improve overall health outcomes.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".