Deficits in urological knowledge and skills among family medicine residents in Canada
Bibliographic record
Abstract
INTRODUCTION: The last 10-15 years has seen a decline in formal undergraduate urological education throughout Canada. Given the large volume of urological presentations in family practice, trainees need to acquire the requisite urological knowledge and skills to serve their patients. The objective of this study is to determine the perceived level of urological knowledge and skills among Canadian family medicine residents. METHODS: A 15-item, anonymous, online survey was distributed via email to all Canadian family medicine program directors from September to December 2018 and distributed to their residents. The survey obtained data on demographics, training, undergraduate urology experience, self-reported proficiency in interpreting urological investigations, performing common urological procedures, and managing common urological conditions. Descriptive statistics were used to summarize data. RESULTS: The questionnaire was completed by 142 family medicine residents with representation from Western Canada (27.5%), Ontario (32.4%), and Quebec (40.1%); 39.4% of respondents had completed a urology rotation during medical school and only 29.1% felt that their medical training prepared them for the urological aspects of family medicine. Although the majority of respondents felt proficient in performing a digital rectal examination (58.5%) or managing urinary tract infections (97.9%), only a minority felt competent in performing male genitourinary examination (40.1%), uncomplicated male (34.5%), female (45.8%) or difficult (9.2%) urethral catheterization. Likewise, the minority of respondents felt comfortable managing erectile dysfunction (41.5%), scrotal swelling (34.7%), and scrotal pain (25.7%). CONCLUSIONS: There are significant deficiencies in urological knowledge and skills among family medicine residents in Canada, possibly because of insufficient educational experiences during medical training.
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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 | 0.004 | 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".