The learning styles of graduating Canadian urology residents
Bibliographic record
Abstract
INTRODUCTION: The Kolb learning theory attributes differences in the way people learn with the way they perceive and process an experience, leading to uniquely different learning styles. Studied in other surgical disciplines, it has yet to be examined in a urology population. Identifying the learning style of urology residents may help in the development of teaching curricula that are best suited to knowledge and skill acquisition. The objective of this study was to characterize the learning styles of graduating Canadian urology residents attending the Queen's Urology Exam Skill Training (QUEST) examination. METHODS: The Kolb Experiential Learning Profile (KELP) 4.0 questionnaire was administered to all graduating Canadian urological residents attending QUEST for the years 2021-24. Project participation was 100%. All participants received a report at the conclusion of the course. Participants' preferred learning phase (acting, thinking, reflecting, experiencing) and a specific learning style (deciding, analyzing, thinking, acting, initiating, balancing, reflecting, experiencing, imagining) were identified for all residents. Preferred learning phase and learning style were compared among years using the Chi-squared test (α=0.05). Preferred learning phase and learning style were compared amongst self-identified gender using the Fisher-Freeman-Halton exact test (α=0.05). RESULTS: Graduates from 2021 (n=35), 2022 (n=29), 2023 (n=37), and 2024 (n=35) were included. In aggregate, the preferred learning phases among urology residents included thinking (38%, n=51), followed closely by acting (32%, n=44). A minority of urology residents preferred the reflecting (21%, n=28) and experiencing phase (10%, n=13). There were no significant differences year to year within an individual learning phase (p>0.05). In aggregate, the most common preferred learning styles included the deciding learning style (21%, n=29), followed by analyzing (15%, n=20), thinking (14%, n=19), acting (13%, n=18), and initiating (13%, n=18). Lesser common learning styles included balancing (11%, n=15), reflecting (7%, n=10), imagining (3%, n=4), and experiencing (2%, n=3). There were no significant differences year to year within an individual learning style (p>0.05). There were no significant differences between self-identified gender and preferred learning phase or style (p>0.05). CONCLUSIONS: Graduating Canadian urology residents vary in their preferred learning styles, but the majority seem to learn by acting and thinking. A non-trivial number of learners displayed learning styles under-represented in surgical specialties. This lays the groundwork for future studies correlating learning style to exam performance and identifying predictors of successful completion of residency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".