Knowledge and confidence level of Canadian urology residents toward biostatistics: A national survey
Bibliographic record
Abstract
INTRODUCTION: Adequate knowledge of biostatistics is essential for healthcare providers to stay up to date with medical advances and maintain an evidence-based practice. However, training in clinical research in Canadian residency programs varies considerably. Our study aimed to determine Canadian urology trainees' knowledge of biostatistics and interpretation of the scientific literature. METHODS: We conducted a national survey of all Canadian urology residents and fellows, which assessed experiences with biostatistics, self-reported confidence with statistical questions, and knowledge of biostatistical concepts. RESULTS: Out of 201 urology trainees, 74 (36.8%) responded to the survey. The majority of respondents disagreed or strongly disagreed with the fact that they had sufficient training in biostatistics in medical school (67.6%) or that their current knowledge was sufficient for the rest of their career (66.1%). If given the chance, 82.3% of respondents would like to learn more about biostatistics. About half of respondents were able to correctly identify ordinal variables (51.5%), discrete variables (52.9%), or interpret adjusted odds ratios (50.0%). Despite senior residents reporting more confidence on biostatistical questions, our results did not demonstrate significant differences in overall knowledge according to level of training or experience with biostatistics. CONCLUSIONS: Our results identified important knowledge gaps among current Canadian urology trainees. Most trainees do not believe they have sufficient training in biostatistics. Knowledge of basic statistical concepts was lower than expected and did not significantly differ according to level of training. Our results highlight the need for structured, dedicated training in biostatistics for urology trainees within the new Competence by Design teaching framework.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".