Educational background, professional experience, and research productivity of Canada's academic ophthalmology leadership
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to assess the educational background, professional experience, and research productivity of Canada's academic ophthalmology leadership. METHODS: This cross-sectional study focused on leaders from ophthalmology departments at 15 Canadian universities, as well as the Canadian Journal of Ophthalmology (CJO) editorial board and the Canadian Ophthalmological Society (COS) board of directors. RESULTS: Ninety-one academic leaders were identified, which included 15 chairs, 9 vice chairs, 17 hospital chiefs, and 15 program directors. Additionally, the 10 members of the COS board of directors and the 31 members of the editorial board of the CJO were included. The duration of professional experience was the longest for hospital chiefs (26.63 ± 7.08 years) followed by chairs (23.86 ± 6.11 years) (p < 0.001). Chairs had the largest mean number of publications (87.13 ± 73.17), followed by vice chairs (70.89 ± 78.29) (p = 0.012). The most common residency programs attended by position holders were offered by the University of Toronto, followed by McGill University. Forty-three academic ophthalmology leaders graduated from U.S. fellowship training programs (48.3%). CJO editors were most likely to have a professor appointment (p = 0.002), fellowship training (p = 0.042), U.S. fellowship training (p = 0.007), a larger number of publications (p = 0.006), and a greater h-index (p = 0.001). CONCLUSION: Chairs followed by vice chairs demonstrated the highest mean number of publications and h-index. More than half of the academic leaders had fellowship training either in the U.S. or Toronto. Prospective ophthalmologists interested in academic leadership may leverage these data to strategically guide their professional careers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".