Undergraduate exposure to urology: impact of the distributed model of medical education in British Columbia
Bibliographic record
Abstract
Background: With the increased development of distributed sites for medical education across Canada, it is imperative we ensure that the quality of education is comparable between the different campuses. Our objective was to assess medical student experience and comfort with common urologic clinical encounters and to determine whether any differences exist between the distributed education sites at the University of British Columbia (UBC).Methods: Questionnaires assessing urologic education were delivered simultaneously to all final-year UBC medical students attending campuses in Vancouver, Victoria and Prince George. Results were analyzed using descriptive statistics.Results: Overall, 55.8% of students felt their exposure to urology was adequate in the medical curriculum; learners in the Northern Program (Prince George) ranked their clinical and didactic experiences significantly higher. Areas requiring improvement include teaching of the male genitourinary exam, digital rectal exam and sexual history, in which learners rated teaching “good/outstanding” in only 18.2%, 47.7% and 43.2% of cases, respectively. Overall, students were most comfortable with the following clinical encounters: urinary tract infection, nephrolithiasis, benign prostatic hyperplasia, hematuria, incontinence and prostate cancer. Few differences in student experience or comfort were noted related to campus site, gender or urology clerkship exposure.Conclusion: A significant minority of learners perceived that theyhad inadequate exposure to urology in the undergraduate curriculum. Experience in urology was comparable across the distributed sites and was congruent with teaching objectives. Students were comfortable with the clinical scenarios deemed most important in the literature. Learners in the Northern Program were significantly more satisfied with their urologic teaching, which potentially highlights the advantages of learning in a smaller academic setting.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".