The newly graduated Canadian urologist: Over-trained and underemployed?
Bibliographic record
Abstract
INTRODUCTION: There are two prevailing perceptions among urology residents (1) fellowship training is becoming a requirement after residency, and (2) there are few job opportunities after graduation. In this study, we examine postgraduate training patterns and employment choices of urology residents. METHODS: All Canadian urology program directors provided a summary of fellowship training and employment of Canadian residents graduating between 1998 and 2009. Logistic regression models were used to detect linear trends. RESULTS: In total, 258 Canadian urology residents graduated over the study period, with a median of 22 (interquartile range 21-22) graduating per year. Of these, 72% completed a fellowship. Of these fellowships, 62% included protected research time. The most common subspecialty area was minimally invasive surgery (MIS)/endourology (39% of fellowships). There was a significant increase in fellowship training over time (p < 0.0001); this was mostly due to an increase in MIS/endourology fellowships. The number of urologists obtaining graduate degrees after medical school has increased significantly over the study period. Almost all graduates are employed. Of the employed graduates in total, 34% are academic urologists. Among all graduates, 50% are practicing within 100 km of their residency site, 16% are practicing in the United States and 22% are in rural practice. There has been no significant change over time in the proportion of residents practicing within 100 km of their training program, practicing rurally, leaving their province of training, practicing in the United States, or choosing academic practice. CONCLUSIONS: Fellowship training, especially in MIS/endourology, has become significantly more common. Graduate degrees are more frequently being obtained. We did not find evidence that there has been a significant change in a urology resident's ultimate ability to obtain employment upon graduation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".