Scholarly Impact of Academic Ophthalmologists and Vision Scientists in Canada
Bibliographic record
Abstract
OBJECTIVE: To outline the current impact of Canadian ophthalmology and vision science research as measured by novel research metrics. DESIGN: Cross-sectional survey. PARTICIPANTS: All Canadian ophthalmologists (n = 687) and vision scientists (n = 119) with an online bibliometric profile and academic appointment at a major ophthalmology training centre were included. METHODS: Faculty lists of Canada's 15 major academic ophthalmology departments were obtained. Faculty names, appointments, sex, and educational background were recorded. Elsevier's Scopus database was used to calculate H-index, m-quotient, and total citations for each faculty member. Details around grant funding were obtained through the Canadian Institutes of Health Research (CIHR) Funding Decisions Database. RESULTS: Average H-indices were 7.42 ± 7.98 for ophthalmologists and 23.78 ± 15.25 for vision scientists. Higher academic appointment was correlated with higher h-indices and m-quotients (p <0.0001 for both). Most academic departments had significantly more males than females (avg. 71% male, 29% female); however, more equal ratios were seen in faculties in Quebec. No significant differences in research impact were identified between male and female ophthalmologists when controlled for academic appointment and career stage (p > 0.05). In clinical ophthalmology research, the top three departments with the highest average H-indices were Western University, the University of Toronto, and Dalhousie University. The University of British Columbia, Université de Montréal, and McGill University received the most funding from the CIHR in the last 10 years. CONCLUSION: This study highlights the current scope of ophthalmology and vision science research in Canada. Important trends were identified in research productivity across academic rank, sex, and clinical subspecialty.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.037 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".