Attitudes of graduating Canadian urology residents on the job market: Is it getting better or are we just spinning our wheels?
Bibliographic record
Abstract
INTRODUCTION: There has been increasing awareness of employment difficulties for physicians, especially surgeons, in Canada over the past few years. Our objective was to elucidate the attitudes and experiences of graduating Canadian urology residents in obtaining employment. METHODS: We surveyed four separate cohorts of graduating urology residents in 2010, 2011, 2016, and 2017. Responses from the 2010 and 2011 cohorts were combined and compared to the combined results of the 2016 and 2017 cohorts. Mean Likert responses were compared using unpaired t-tests. An agreement score was created for those responding with "strongly agree" and "agree" on the Likert scale. RESULTS: A total of 126 surveys were administered with a 100% response rate. The job market was rated as poor or very poor by 64.9% and 58.4% of graduates in 2010/2011 and 2016/2017, respectively (p=0.67). Lack of resources was identified as the biggest barrier to improved employment in both cohorts. Networking at meetings and staff urologists at their institution were the most important factors aiding employment identified by both cohorts. The ideal practice was academic or academically associated community practices in a large urban area, with 5-10 partners for both cohorts. CONCLUSIONS: The majority of graduating urology residents viewed the job market as poor or very poor and this did not change over a six-year period. It is unclear how much personal preference for location and practice type drove the somewhat negative outlook of employment opportunities, as the majority of residents were seeking large urban, academic, or academically associated community practices in competitive locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 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".