Low vision device coverage across Canada
Bibliographic record
Abstract
Purpose: Low vision devices can play a significant role in improving the quality of life of the visually impaired. Because each Canadian province and territory is responsible for how health care is delivered, government coverage for devices varies between jurisdictions. This article provides a concise summary of the different provincial and territorial low vision device subsidies available to visually impaired adults in Canada. Methods: Information gathered for this article was obtained from organizations such as Vision Loss Rehabilitation Canada, health care professionals (including ophthalmologists and optometrists) across Canada and from government agencies providing low vision services. Details regarding government assistance for low vision devices include the program name, administering organization, eligibility, types of devices that are subsidized and how the assistance is administered in each province and territory. Links to government websites for device coverages are provided in the article where applicable. Results: Within the 10 provinces and 3 territories of Canada, there is some form of financial assistance for low vision devices available to the adult population in 54% (7/13) of the jurisdictions. At present, subsidization is quite variable between jurisdictions, ranging from full coverage to no provincial/ territorial coverage whatsoever. Furthermore, while there is some coverage in Manitoba and New Brunswick, it is limited to post-secondary education and work-related needs. Conclusion: Adults with low vision in Canada cannot always rely on public support to obtain low vision devices. Further legislation and development of coverages is needed to provide more unified and equitable access to devices for all Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".