Ophthalmology as a career choice among medical students: a survey of students at a Canadian medical school
Bibliographic record
Abstract
BACKGROUND: There is a lack of investigations into the factors that lead medical students to pursue increasingly competitive post-graduate training programs. We sought to determine the factors that influence medical students' opinions on ophthalmology as a career and on ophthalmological medical education. METHODS: An anonymous 36-question survey was distributed to all medical students across the four program years at the Schulich School of Medicine and Dentistry as a non-probabilistic convenience sample. Survey results were analyzed using Mann-Whitney U tests to determine significant differences between study sub-populations. Multivariate regression analysis was performed to identify correlates for positive views towards ophthalmology. RESULTS: 81% of questions had a mean positive response amongst the students. Students held negative views regarding the amount of exposure to ophthalmology in medical school. The greatest differences in opinion regarding ophthalmology were seen between those with more exposure and interest in ophthalmology compared to their counterparts with less. Regression analysis identified interest in ophthalmology as a significant correlate to a positive opinion in the field. CONCLUSIONS: Our survey demonstrates that while most students had positive views about ophthalmology, some aspects were viewed negatively. Students felt there was a lack of exposure, both educationally and clinically to ophthalmology, which may contribute to some misconceptions of the field. Early exposure appeared to be critical to forming positive opinions of ophthalmology and could be emphasized in medical education.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".