Creating a Healthcare Entrepreneurship Teaching Program for Medical Students
Bibliographic record
Abstract
Introduction: The future of healthcare hinges on effective adoption of innovative solutions. Arguably, physicians are ideally positioned to propel clinical innovation given their firsthand experience with healthcare challenges; however, physicians often lack the necessary skills in innovation development and implementation methodology. The gap is partly a result of the paucity of exposure to innovation and entrepreneurship concepts within medical education and postgraduate training. To address this gap, the University of Toronto’s distributed medical education campus in Mississauga created a novel teaching initiative designed to impart themes of healthcare entrepreneurship to early stage medical learners. Methods: To inform the design of the program, the authors conducted a series of semi-structured interviews with key stakeholders, including physician entrepreneurs, innovation leaders, curriculum specialists and medical students. Using thematic analysis, key recommendations were extracted regarding learning objectives, approach to program delivery, and anticipated outcomes. A well-established entrepreneurial teaching model, the MaRS Entrepreneurship Framework, was adapted to frame the curricular content to the needs of medical learners. The resulting educational product consisted of six sessions, taught by subject matter experts, which outlined a methodological approach to the development of a medical start-up as a means of launching an innovation. Results: From November 2019 to May 2020, six sessions were held with a total of 37 unique attendees. The authors found that the series generated interest in entrepreneurship among medical students while fostering an appreciation for the basic principles of entrepreneurship. Conclusion: The next stage involves further program evaluation to guide the next iteration of the program. Potential avenues for growth include delivering the series virtually to support greater student accessibility. Future considerations include incorporating entrepreneurship into core undergraduate medical curricula and creating a dual degree program in medicine and entrepreneurship that cater to students with a deep interested in the field of healthcare entrepreneurship. Disclosure: The authors have no conflict of interest to declare. As all data were completely anonymized and no patients were involved, this was not reviewed by an ethics board.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.007 |
| 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".