A scoping review of vision rehabilitation services in Canada
Bibliographic record
Abstract
Around 1.5 million Canadians live with some form of vision impairment. The demand for vision rehabilitation (VR) services is projected to increase as the number of older adults with age-related vision loss rises. To inform programmes and policies for VR, we aimed to answer two research questions: (1) How are VR services delivered in Canada? and (2) If gaps exist in current delivery of VR services, how can they be characterized? We used Arksey and O’Malley scoping review framework. A comprehensive search of five databases (PubMed, CINAHL/EBSCO, EMBASE, ProQuest, and Global Health) was performed during January 2019 and then updated in March 2021. Index terms and keywords relating to vision loss or impairment and rehabilitation were used. Non-peer-reviewed (grey) literature, in the form of reports and policies on VR in Canada, was sourced via Google/Google Scholar. To be included, sources had to (1) focus on VR services in Canada, (2) be available in English or French, and (3) be published after 2003. Data were extracted and analysed thematically to describe VR services across provinces and to identify gaps in service delivery in Canada. Out of 1311 studies identified, 62 were included. Findings indicate that the structure of VR services as well as provincial funding for assistive devices varies across provinces. The reported gaps at the level of service providers, users, and delivery systems were lack of awareness about the benefits of VR, limited collaboration and coordinated services between eye care and VR services, delayed referral to VR, shortage of specialists, and insufficient funding and training for vision devices. This article describes VR services in Canada and documents important gaps in VR services and research evidence across provincial jurisdictions. Future work to address gaps, and develop and evaluate interventions to facilitate optimal VR services is imperative.
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 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 | 0.020 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.044 | 0.074 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".