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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.024 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".