Assessing the impact of digital health literacy on health management practices in Arab Middle Eastern and North African countries: insights from predictive modeling
Bibliographic record
Abstract
Background: Digital health literacy is a critical digital determinant of health (DDoH) in the Arab Middle East and North Africa (MENA) region, where technological disparities, limited healthcare infrastructure, and diverse socio-cultural contexts significantly impact healthcare access and management. Objective: This study evaluates the impact of digital health literacy on health management practices and ensuing health outcomes in Arab Countries, employing predictive modeling as an analysis tool to uncover key determinants. Methods: A cross-sectional survey of 12,522 respondents from ten Arab MENA countries was analyzed to examine relationships between survey features and health outcomes. We compared multinomial regression to machine learning models, including CatBoost and Random Forest, to predict outcomes and identify significant predictors. Results: CatBoost, a powerful ML model that handles categorical data efficiently, achieved a predictive accuracy of 97.8%, outperforming other models in capturing complex, nonlinear relationships. Five key determinants of digital health literacy on health management outcomes were identified: limited internet access, restricted health service access, confidence in AI health resources, health monitoring tool usage, and social media health information consumption. Conclusion: Enhancing digital health literacy is critical for improving healthcare outcomes in the Arab MENA region. This study underscores the need for culturally tailored digital health interventions to address regional technological and healthcare challenges. Policymakers must prioritize these strategies to reduce disparities and empower individuals in managing their health.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".