The Impact of Previous Cardiology Electives on Canadian Medical Student Interest and Understanding of Cardiology
Bibliographic record
Abstract
Background: Most Canadian medical schools do not have mandatory cardiology rotations. Early exposure to clinical cardiology aids career navigation, but clerkship selectives are chosen during pre-clerkship. This study investigates whether prior elective experiences affect medical student interest as well as understanding of cardiology before clerkship selections. Methods: A literature search was conducted using Google Scholar, Embase and PubMed to create an evidence-based cross-sectional survey. The anonymous questionnaire was administered to 53 second-year medical students at a Canadian medical school via Opinio, an online survey platform. Students were assessed on their interest and understanding of cardiology practice using a 5-point Likert Scale. Descriptive statistics and Chi-Square analysis were applied to assess the relationship between previous elective experience, medical student interest, and understanding of career-related factors pertaining to cardiology. Results: Overall, 26 (49.1%) students reported cardiology interest, while it was a preferred specialty for 9 (17.0%). Medical students reported low understanding of community practice (n=20, 37.7%), duration of patient relationships (n=14, 26.4%), spectrum of disorders (n=13, 24.5%), and in-patient care (n=11, 20.8%) associated with cardiology practice. Students with prior cardiology electives had increased understanding of in-patient care (χ2 = 4.688, Cramer’s V = 0.297, p = 0.030 and were more likely to select cardiology as a top specialty choice (χ2 = 7.983, Cramer’s V = 0.388, p = 0.005). Conclusions: Pre-clerkship medical students have a low understanding of cardiology practice. Increasing pre-clerkship exposure to cardiology may help students determine their interest in the specialty before clerkship selectives are chosen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".