Diversity gaps among practicing ophthalmologists in Canada: a landscape study
Bibliographic record
Abstract
OBJECTIVE: Outline geographic disparities in access to language-and gender-concordant ophthalmologic care in Canada. DESIGN: Cross-sectional. PARTICIPANTS: Practicing ophthalmologists in Canada (September 2023). METHODS: Data on ophthalmologists, including demographics, languages spoken, and practice locations, were collected from provincial regulatory body websites. Population data were extracted from the 2021 Statistics Canada Census. Ratio of ophthalmologists-to-potential patients and mean distances (absolute, population-weighted) to gender- and language-concordant care were calculated. The five most common languages spoken in the included provinces were analyzed. RESULTS: There were 986 and 1338 ophthalmologists in the language and gender analysis, respectively. Few ophthalmologists spoke non-official languages in Saskatchewan, Manitoba, and Nova Scotia. In a population-weighted analysis, the distance to a language-concordant ophthalmologist were 4.55 times greater for Spanish speakers compared to their English counterparts. Cantonese speakers had the shortest distances to language-concordant care but were still had 40% greater distance than English speakers in the same regions. Despite French-speaking ophthalmologists being the most prevalent per 100 000 speakers, francophones outside Quebec endured distances over double that of anglophones to access language-concordant care. Females in Newfoundland and Saskatchewan faced the longest distances to access gender-concordant care. In Ontario, females may face 3 times the distance to gender-concordant ophthalmologists compared to males. Quebec approaches gender parity with a male-to-female ratio of 55:45. CONCLUSIONS: The results highlight the disparities in accessibility to non-English ophthalmologic care and the underrepresentation of female ophthalmologists across Canada. These disparities underscore the need for targeted strategies to ensure that the ophthalmologic workforce mirrors the demographic of the population it serves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".