Supply and demographic characteristics of Ontario’s ophthalmologists from 2010 to 2019: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: With an aging population in Ontario, ophthalmologists provide most of their care to older adults, which has prominent human resource implications. In this study, we sought to investigate the supply and demographic characteristics of Ontario's ophthalmologists. METHODS: In this retrospective, population-based analysis, we evaluated cohort demographics, including sex and career stage, of Ontario's ophthalmologists from 2010 to 2019, which we reported using descriptive statistics. Similarly, we detailed ophthalmologist supply within different areas of care using descriptive statistics. RESULTS: Over the study period, a median of 464 ophthalmologists were practising in Ontario each year. The proportion of female ophthalmologists increased from 18.7% in 2010 to 24.1% in 2019. The proportion of late-career ophthalmologists (aged > 55 yr) significantly increased by 6.4% over the study period and constituted 45.3% of the workforce in 2019. Comprehensive cataract surgery was the most common area of care. Although the number of ophthalmologists per 100 000 people remained stable over the study period (3.27 ophthalmologists/100 000 people in 2019), the number of ophthalmologists per 100 000 people aged 65 years and older fell by 18.4% from 2010 to 2019. The greatest supply reduction was among moderate-volume comprehensive cataract surgeons (-20.2% overall and -35.4% relative to the population aged ≥ 65 yr). INTERPRETATION: Between 2010 and 2019, the overall number of ophthalmologists in Ontario remained stable; however, we observed declines in the number of ophthalmologists per 100 000 people aged 65 years and older for most areas of care. Nearly half of the ophthalmology workforce is now older than 55 years and female representation is increasing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".