Social Determinants of Health and Barriers in Accessing Eye Care for Refugees in the Greater Toronto Area
Bibliographic record
Abstract
Purpose: To evaluate ocular health status, vision-related quality of life, and access to eye care services among a multi-ethnic refugee population in the Greater Toronto Area (GTA). Methods: Participants completed a structured survey capturing demographic, medical, and vision-related data. Descriptive statistics were employed to summarize and interpret the responses. Canadian population data were sourced from Statistics Canada and National Vision Health Reports. Participants were recruited during ophthalmic screening outreach visits at four refugee housing sites in the GTA, and were eligible if they were adults or mature minors residing in refugee homes after arriving in Canada as refugees or asylum seekers between October 2022 and October 2024. Proportions were converted to estimated counts for comparison, and harmonized categorical variables were analyzed using Pearson's Chi-Square or Fisher's Exact Test. Bivariate and multivariate logistic regression models were then used to assess associations between demographic, clinical, and psychosocial factors and two outcomes: difficulty coping with life due to vision and history of barriers to eye care. Results: Among 94 refugee participants (mean age 46.5 years; 41% female), rates of recent eye exams (19.1%) and prescription glasses use (51.1%) were significantly lower than in the Canadian population (74.5% and 81.0%, respectively; p<0.0001). Over half (55.3%) were dissatisfied with their vision, and financial barriers (50%) were the most reported obstacle to care. Prior abuse was associated with greater odds of encountering barriers (OR=7.65, p=0.005), while dissatisfaction with vision (OR=0.11, p=0.025) and interference with daily activities (OR=233.0, p<0.0001) strongly predicted difficulty coping. Conclusion: Refugees face significant vision-related health disparities. Interventions should address access, government benefits, education, and psychosocial supports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".