A survey of urological manpower, technology, and resources in Canada.
Bibliographic record
Abstract
INTRODUCTION: Knowledge of the current status of manpower and resources is important in understanding the state of any medical specialty, and critical in planning for future recruitment, funding and infrastructure development. METHODS: In 2003, the Canadian Urological Association (CUA) conducted two nationwide surveys examining manpower, resources, and the technology available. One survey went only to academic and hospital leaders across the country (the resources survey), while the other was sent to the entire general membership of the CUA. RESULTS: The response rate for the resources survey was 67%, while that for the membership survey was 50.4%. The respondents' ages were evenly distributed, with the modal 5-year range being 51 to 55 years of age. Eighty-eight percent of respondents were Canadian-trained. Two-thirds of respondents spent over 80% of their practice time in direct patient care, and most practiced general urology. The majority of respondents practiced in smaller hospitals: 57.6% in centres with 300 or fewer inpatient beds, and 47.2% of centres reported < 500 procedures/year. Community hospitals (62% of responses to the resources survey) generally had fewer advanced technologies than academic centres. A quarter of the cystoscopy equipment used by respondents was over 15 years old. CONCLUSIONS: The results of these surveys present a snapshot of the current state of urology resources and manpower across Canada, potentially allowing better planning and negotiations with hospitals and governments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".