Perception, career choice and self-efficacy of UK medical students and junior doctors in urology
Bibliographic record
Abstract
INTRODUCTION: There is a growing concern about the reduced clinical exposure to urology at undergraduate level in the United Kingdom. As a consequence, the competencies of junior doctors are considered inadequate. The views of these doctors in training towards urology remain under reported. METHODS: A modified Delphi method was employed to construct a questionnaire. Given the rise of social media as a platform for scientific discussion, participants were recruited via a social networking site. Outcomes assessed included career preference, exposure to urology, perceived male dominance, and confidence at core procedures. RESULTS: In total, 412 and 66 responses were collected from medical students and junior doctors, respectively. Overall, 41% of participants felt that they had received a good level of clinical exposure to urology as part of their training and 15% were considering a career in this speciality. Female students were significantly less likely to consider urology as a career option (p < 0.01). Of these, 37% of the students felt confident at male catheterization and 46% of students regarded urology as a male-dominated speciality. CONCLUSIONS: Urology is perceived as male dominated and is the least likely surgical speciality to be pursued as a career option according to our survey. Increased exposure to urology at the undergraduate level and dedicated workshops for core urological procedures are needed to address these challenges.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".