Mobile Eye Units in the United States and Canada: A Narrative Review of Structures, Services and Challenges
Bibliographic record
Abstract
Background and Objectives: Mobile Eye Units (MEUs) have emerged as practical innovations to overcome geographic, financial, and systemic obstacles to eye care. Although numerous programs operate across the United States and Canada, a narrative review describing their structure, implementation and services, remain limited. This narrative review examines various MEUs models in the United States and Canada, using real-world examples to highlight each model’s structure, services, populations served, and key benefits and limitations. Methods: We performed a narrative review of peer-reviewed and gray literature published from 1990 to August 2025, identifying mobile eye units in the United States and Canada. Programs were grouped into four operational models based on services, equipment, and implementation characteristics. Ophthalmology residency program websites in the United States were also reviewed to assess academic involvement in mobile outreach. Results: We identified four operational MEU models: Fully Equipped Mobile Units (FEMUs), Semi-Mobile Outreach Units (SMOUs), School-Based Vision Mobile Units (SBVMUs), and Hybrid Teleophthalmology Units (HTOUs). FEMUs provide comprehensive on-site diagnostic capabilities but require substantial financial and logistical resources. SMOUs are lower-cost and flexible but offer more limited diagnostics. SBVMUs facilitate early detection in children and reduce school-based access barriers but depend on school coordination. HTOUs expand specialist interpretation through remote imaging, although their success relies on reliable digital infrastructure. Across all models, follow-up and continuity of care remain major implementation challenges. Approximately 21% of U.S. ophthalmology residency programs publicly report involvement in mobile outreach. Conclusions: MEUs play a critical role in reducing geographic and structural barriers to eye care for underserved populations across United States and Canada. However, limited outcome reporting, particularly regarding follow-up rates and continuity of care, hinders broader assessment of their effectiveness. Strengthening the integration of MEUs with patient navigators, integrated electronic health record, insurance support and support of local health networks is essential for improving long-term sustainability and impact.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".