Curriculum Hours and Approaches to Instruction in Veterinary Ophthalmology: A Global Survey of Veterinary Schools
Bibliographic record
Abstract
Reports regarding curricula in ophthalmology across veterinary schools are not currently available. The objective of this study was therefore to investigate the number of contact hours and approaches to teaching ophthalmology in the curriculum of English-speaking veterinary schools worldwide. An online survey was distributed to 51 veterinary colleges in North America, the United Kingdom, Australia, New Zealand, and the Caribbean. Questions pertained to hours dedicated to didactic and laboratory-based instruction, species used, final-year rotations, in-person compared with online instruction, and effective and less effective approaches to teaching veterinary ophthalmology. Descriptive statistics of the quantitative survey responses and a thematic analysis of the open-ended responses were conducted, respectively. A 71% ( n = 36/51) response rate was recorded, and the average number of American or European board-certified ophthalmologist instructors per veterinary college was 2.33. Total didactic contact hours varied from 6 to 63 hours ( M = 25.6 ± 15.7 hours), and total laboratory contact hours varied from 0 to 153 hours ( M = 25.47 ± 38.17 hours), mainly occurring in the fourth year. Dogs were the most used species in surgical exercises (40%). Final-year rotations occurred in 88% of schools, and 88% of instruction was conducted in person across all schools. Case-based learning, review of basic sciences, and use of video were identified as effective didactic teaching strategies by 72% (26/36), 47% (17/36), and 31% (11/36) of schools, respectively. This report can serve as a reference for future studies guiding curricular delivery in veterinary ophthalmology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".