A survey of senior medical students’ attitudes and awareness toward teaching and participation in a formal clinical teaching elective: a Canadian perspective
Bibliographic record
Abstract
BACKGROUND: To prepare for careers in medicine, medical trainees must develop clinical teaching skills. It is unclear if Canadian medical students need or want to develop such skills. We sought to assess Canadian students' perceptions of clinical teaching, and their desire to pursue clinical teaching skills development via a clinical teaching elective (CTE) in their final year of medical school. METHODS: We designed a descriptive cross-sectional study of Canadian senior medical students, using an online survey to gauge teaching experience, career goals, perceived areas of confidence, and interest in a CTE. RESULTS: Students at 13 of 17 Canadian medical schools were invited to participate in the survey (4154 students). We collected 321 responses (7.8%). Most (75%) respondents expressed confidence in giving presentations, but fewer were confident providing bedside teaching (47%), teaching sensitive issues (42%), and presenting at journal clubs (42%). A total of 240 respondents (75%) expressed interest in participating in a CTE. The majority (61%) favored a two week elective, and preferred topics included bedside teaching (85%), teaching physical examination skills (71%), moderation of small group learning (63%), and mentorship in medicine (60%). CONCLUSION: Our study demonstrates that a large number of Canadian medical students are interested in teaching in a clinical setting, but lack confidence in skills specific to clinical teaching. Our respondents signaled interest in participating in an elective in clinical teaching, particularly if it is offered in a two-week format.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".