Entrepreneurship in Medical Education: Evaluation of the Entrepreneurship in Healthcare Seminar Series
Bibliographic record
Abstract
Background: Innovation and entrepreneurship are central to healthcare. Physicians are ideally positioned to create sustainable healthcare innovations. However, few medical programs in Canada provide training for students to pursue innovation and entrepreneurship. We created the Entrepreneurship in Healthcare Seminar Series (EHSS), a novel initiative designed to teach medical students about developing innovations and launching entrepreneurial ventures. We evaluated the EHSS based on medical student feedback and suggest future directions. Methods: EHSS consisted of seven sessions per academic year, each led by a physician-entrepreneur. The session topics outlined a methodological approach to developing a medical start-up, from ideation to implementation, including a formal talk and question/answer period. Quantitative and qualitative evaluations of the program were acquired from anonymized feedback forms with quantitative questions rated on a 5-point Likert scale. Results: From October 2020 to May 2022, there were a total of 258 unique attendees, of which 199 completed feedback forms (77.1%). 88% of attendees agreed or strongly agreed that the sessions were engaging and well-organized, learning objectives were met, and skills gained will be useful in practical settings. Three key themes arose from attendee open-text responses: importance of aligning personal and professional values with entrepreneurial pursuits, significance of mentorship, and that innovation requires proactive identification of healthcare gaps and creative solutions. Discussion: Through EHSS, medical students gained a comprehensive overview of medical entrepreneurship and networking opportunities with physician-entrepreneurs. Future work involves expanding the seminar series to include an experiential learning component to apply foundations learnt from the lectures while receiving mentorship.
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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 | 0.034 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".