Ophthalmology teaching in Australian medical schools: A national survey
Bibliographic record
Abstract
To survey the current educational trends and methods of ophthalmology teaching in Australian undergraduate and postgraduate medical schools. Cross-sectional survey; National online survey distributed to Australian university undergraduate and post-graduate medical schools from November 2020 to March 2021. The survey encompassed 35 questions on student demographics, teaching methods, core theoretical topics, clinical skills, and assessment methods in ophthalmology. One survey per institution completed by the relevant individual responsible for curriculum. Total response rate of 90.48% (19 of 21 medical schools) was received with good representation across Australia. Ophthalmology rotations were required in 63.3% (n = 12), while 36.7% (n = 7) did not have mandatory terms. This compares favourably to the USA (16%), Canada (35.7%) and equivalent to UK (65%). 74% (n = 14) state ophthalmology is not a priority in the curriculum. All respondents reported student exposure to at least one clinical day in ophthalmology, with total teaching time ranging from less than six hours (36.9%), up to greater than two weeks (10.5%). Overall, only 31.6% reported utilisation of the International Council of Ophthalmology (ICO) curriculum in curricular development. Ophthalmology medical school teaching in Australia remains reasonable when compared internationally, but there is significant variation amongst universities. Incorporation of the ICO curriculum and development of shared resources would enhance medical graduates’ competence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".