Socioeconomic status and vision care utilization in Canada: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Despite a universal health care system, access to vision care in Canada is not necessarily equally accessible to all patients. The purpose of this review was to explore the association between socioeconomic status (SES) and vision care utilization in Canada. METHODS: Medline, Embase, CINAHL, and Cochrane were searched from inception to January 2024 for relevant articles containing original data. Studies that explored the association between SES and vision care utilization in Canadian patients were included. Risk of bias was assessed using the Newcastle-Ottawa and AXIS assessment tools. Descriptive statistics were used to summarize findings. The review was registered in PROSPERO (registration number: CRD42024502482) and followed PRISMA guidelines. RESULTS: The search yielded 2,670 records with 23 studies included in this review. The included studies covered all provinces and ranged in date between 1985 and 2022. The included studies explored the relationship between SES and utilization of ophthalmic care, optometric care, or both. Overall, 17 of the 23 studies found that patients of lower SES were significantly more likely to have decreased usage of vision care. Decreased vision care utilization was found for all optometry, ophthalmology care, and diabetic retinopathy screening, as well as for patients of all ages, and in all provinces. DISCUSSION/CONCLUSION: Low socioeconomic status was consistently associated with decreased vision care utilization for patients of all ages. Efforts are required to increase accessibility to vision care for low-income individuals and to improve health equity.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".