Geographic Availability To Optometry Services Across Canada: Mapping Distribution, Need And Self-Reported Use
Bibliographic record
Abstract
Abstract Background: This research investigates the distribution of optometrists in Canada relative to population health needs and self-reported use of vision services.Methods: Optometrist locations were gathered from provincial regulatory bodies. Optometrist-to-population ratios (i.e. the number of providers per 10,000 people at the health region level) were then calculated. Utilization of vision care services was extracted from the Canadian Community Health Survey (CCHS) 2013-2014 question regarding self-reported contacts with optometrists or ophthalmologists. Data from the 2016 Statistics Canada census were used to create three population ‘need’ subgroups (65 years and over; low-income; and people aged 15 and over with less than a high school diploma). Cross-classification mapping compared optometrist distribution to self-reported use of vision care services in relation to need. Each variable was converted into three classes (i.e., low, moderate, and high) using a standard deviation (SD) classification scheme where ± 0.5SD from the mean was considered as a cut-off. Three classes: low (< -0.5SD), moderate (-0.5 to 0.5SD), and high ( > 0.5SD) were used for demonstrating distribution of each variable across health regions. Results: A total of 5,959 optometrists across ten Canadian provinces were included in this analysis. The nationwide distribution of optometrists is variable across Canada; they are predominantly concentrated in urban areas. The national mean ratio of optometrists was 1.70 optometrists per 10,000 people (range = 0.13 to 2.92). Out of 109 health regions (HRs), 26 were classified as low ratios, 51 HRs were classified as moderate ratios, and 32 HRs were high ratios. Thirty-five HRs were classified as low utilization, 39 HRs were classified as moderate, and 32 HRs as high utilization. HRs with a low optometrist ratio relative to eye care utilization and a high proportion of key sociodemographic characteristics (e.g. older age, low income) are located throughout Canada and identified with maps indicating areas of likely greater need for optometry services.Conclusion: This research provides a nationwide overview of vision care provided by optometrists identifying gaps in geographic availability relative to “supply” and “need” factors. This examination of variation in accessibility to optometric services will be useful to inform workforce planning and policies.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".