Bibliographic record
Abstract
ABSTRACT • RÉSUMÉ Background: There has been little investigation into the attitudes and aspirations of current ophthalmology residents.The object of this study was to investigate the factors influencing career choice and to identify the future plans of Canadian ophthalmology residents. Methods: All ophthalmology residents in English Canadian ophthalmology residency training programs were invited to complete an anonymous survey in June 2006. Data were categorized by demographic variables and basic statistics; χ2 comparative analyses were performed. Results: Of 128 residents surveyed,49 (38%) responded to the survey.Having the ability to combine medicine and surgery was the most common factor influencing the decision to pursue ophthalmology (98 % of respondents), with intellectual stimulation (76%) and mentorship (50%) also emerging as important factors.The majority of residents (62%) plan on pursuing fellowship training, with medical retina, anterior segment/cataract, and cornea being the most popular choices (36%, 34%, and 32%, respectively). Most residents expressed plans of pursuing fellowships abroad, and only 22 % planned on training within Canada. Fourteen percent indicated an interest in performing laser refractive surgery, female residents being significantly less likely than males to express such an interest (0 % vs. 21%; p < 0.02). Most residents (71%) expressed the wish to practice in an urban or suburban
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.944 | 0.912 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".