Canadian research contributions to low vision rehabilitation: A quantitative systematic review
Bibliographic record
Abstract
Purpose: Low vision rehabilitation research is a quickly growing area, due in part to the increase in the demand for services geared at older adults with age-related vision loss. Various professions collaborate to provide such rehabilitation services; however, it is currently unclear which profession takes the leading role in advancing the frontiers of low vision rehabilitation research. A recent review article proposed that in Canada, this role is held by physicians. The present study was conducted to replicate these findings under conditions of a systematic review. Method: A search of seven databases and a hand-search of four vision rehabilitation journals identified articles on low vision rehabilitation whose first author had an affiliation at a Canadian institution. Data on professional credentials, funding source, and study content was tabulated. Results: Of the 1,870 references, data from 215 eligible articles were extracted. The top four author credentials were optometrists (with or without PhD; 56 papers, 26.0%), followed by researchers with PhDs only (48 papers, 22.3%), researchers with master’s degrees (43 papers, 20.0%), and medical doctors (with or without PhD; 39 papers, 18.1%). Vision rehabilitation journals published 38 per cent of all papers, followed by ophthalmology (27%) and optometry journals (22%). Publications in the past 11 years amounted to over 50 per cent of the output over the 64-year publication history in this field in Canada, 70 per cent of which were based in universities. Conclusion: The results reflect the mosaic structure of low vision rehabilitation research in Canada, highlighting collaborations among researchers, clinicians, funding sources and rehabilitation agencies. Given its multidisciplinary nature, low vision rehabilitation research seems to be driven by collaboration among the professions.
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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 | 0.065 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.047 | 0.066 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".