Medical students as teachers at CoSMO, Columbia University's student-run clinic: A pilot study and literature review
Bibliographic record
Abstract
BACKGROUND: Although medical students are expected to teach as soon as they begin residency, medical schools have just recently begun adding teacher training to their curricula. Student-run clinics (SRCs) may provide opportunities in clinical teaching before residency. AIM: The aim of this pilot study was to examine students' experiences in clinical teaching at Columbia Student Medical Outreach (CoSMO), Columbia University's SRC, during the 2009-2010 school year. METHODS: A mixed-methods approach was used. Data included closed and open-ended surveys (n = 34), combined interviews with preclinical and clinical student pairs (n = 5), individual interviews (n = 10), and focus groups (n = 3). The transcripts were analyzed using the principles of grounded theory. RESULTS: Many students had their first clinical teaching experience while volunteering at CoSMO. Clinical students' ability to teach affected the quality of the learning experience for their preclinical peers. Preclinical students who asked questions and engaged in patient care challenged their clinical peers to balance teaching with patient care. Clinical students began to see themselves as teachers while volunteering at CoSMO. CONCLUSION: The practical experiences in clinical teaching that students have at SRCs can supplement classroom-based trainings. Medical schools might revisit their SRCs as places for exposure to clinical teaching.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".