Canadian ophthalmology workforce trends from 1971 to 2022: longitudinal analysis of age, sex, and distribution compared to other surgical specialties
Bibliographic record
Abstract
OBJECTIVE: We describe the evolution of the demographics in the Canadian ophthalmology workforce and compare these trends to other surgical specialists. STUDY DESIGN: A longitudinal observational study. PARTICIPANTS: Surgical specialists identified in the Canadian Institute for Health Information's "Supply, Distribution, and Migration of Physicians in Canada" (SDMP) database from 1971 to 2022. Categories included cardiac, general, orthopedic, plastic, and vascular surgery; obstetrics and gynecology; ophthalmology; otolaryngology; and urology. METHODS: Demographic data were extracted from the SDMP database. Descriptive statistics were used to determine mean age, male-to-female ratio, urban-to-rural ratio, percentage of physicians under age 40 and above age 65, Canadian-to-foreign trained ratio, and physicians-to-100,000 population ratio for ophthalmologists and other surgeons by decade. RESULTS: Ophthalmologists and other surgeons aged significantly, and ophthalmologists were significantly older than other surgeons (mean age: 52.15 vs 49.73 years; p < 0.01). The percentage above age 65 doubled for both groups, reaching 21.51% for ophthalmologists and 15.31% for other surgeons. Male-to-female ratios decreased 11-fold for ophthalmologists, now 2.54:1, and 17-fold for other surgeons, now 2.01:1. Surgeon-to-100,000 population ratio increased from 22.97 to 25.53 (p < 0.001), whereas ophthalmologist ratios increased slightly from 3.27 to 3.51 (p < 0.001). Urban-to-rural distribution increased by 34.67% for surgeons but decreased by 32.27% for ophthalmologists. Both groups saw an increase in Canadian-trained practitioners, with ratios rising to 5.23:1 for surgeons and 5.41:1 for ophthalmologists by the 2020s. CONCLUSIONS: The percentage of ophthalmologists and other surgeons above age 65 years has doubled. Ophthalmologists remain significantly older than other surgeons. The ophthalmologists-to-100,000 population ratio, presently 3.51, meets the recommended goal of 3.37-to-100,000 population.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".