Canadian Optometric Low Vision: Predictive Factors and Regional Comparisons
Bibliographic record
Abstract
Purpose: To investigate the regional differences in low vision (LV) provision across Canada and to identify predictive factors for the provision of more extensive low vision services (LVS). Methods: Practising optometrists across Canada were invited to participate in a questionnaire that investigated personal and practice demographics, levels of LVS offered, patterns of referrals and barriers to provision of LVS. Results: 459 optometrists responded. Predictive factors for providing more extensive LVS included: optometrists with >15 years of practice, having a local LV optometrist/ophthalmologist within one day’s travel, not having a multi-disciplinary LV clinic within one-day’s travel, working in a practice in a population of <50,000, and having 2+ optometrists in the same practice. Regional differences were found in the following variables: the presence of an optometrist offering LVS within the respondent’s primary practice, referral criteria, the type of LV provider receiving the referral, and the perceived quality of LVS. Conclusions: LVS are provided differently across Canada and the availability of government-funded LVS appeared to enhance optometric referrals to multidisciplinary low vision clinics. Optometrists who were in a group practice setting, who had practiced for >15 years and who worked in a less populated area were more likely to provide more extensive LVS.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".